main: keep only what reproduces the manuscript; everything else lives on dev
Removed from main (all preserved on the dev branch): the arXiv build and
its sources, design documents (blueprint, results summary, review responses,
essay drafts), tasks/ and CLAUDE.md, the cover letter and reference tooling,
two unused manuscript figures, and every experiment that feeds no figure or
number in the paper: the collapse null, the sexual-vs-asexual lineage, the
NK speciation variant, the 0.5B single-seed LLM prototypes, the compose and
society experiments with their calibration and pilot runs, and their
configs, runners, tests, figure scripts and PBS jobs. Their result bundles
are moved to results/_archive/ (ignored) so the parquets stay on disk.
Also: plot_llm_speciation reads the s{seed}/ layout; the mating-breadth
plot writes under its bundle name; Makefile targets reduced to the kept
experiments; REPRODUCING.md and README point to dev for the rest.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
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parent
ab3dc10587
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# E1 — Distillation without grounding collapses, tail-first
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**Claim tested:** if a model is trained only on the previous model's output, generation after
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generation, does it lose knowledge — and does the *rare* knowledge go first?
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**Setup (Layer 1, pure math).** A "population" of `K = 500` items with a fixed true frequency
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`p*` shaped like a Zipf curve (a few common items, a long tail of rare ones). Each generation we
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draw `n = 100` samples from the current model and refit — **no real data is ever added** (`g = 0`).
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Run for 600 generations, averaged over 100 independent repeats.
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### Symbols
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- **`p*`** — the true frequencies (fixed reality). **`p_t`** — the model's frequencies at generation *t* (drifts).
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- **`H`** heterozygosity = diversity (1 = everything equally likely, 0 = one item left). **`H*`** = diversity of the truth.
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- **forward-KL** `D(p*‖p_t)` — how far the model has drifted from truth (0 = perfect, grows without bound as the tail is forgotten).
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- **support** = how many items still have any probability. **head/tail** = common/rare items.
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### The three panels
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1. **Geometric decay.** Blue = the simulated diversity `H`; black dashed = the exact textbook law
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`H₀·(1 − 1/n)^t`. They sit on top of each other — the loss of diversity is *exactly* the
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population-genetics drift law, not an approximation. (This is the validation gate: if these two
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curves disagreed, the simulator would be wrong.)
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2. **Tail dies first** (log axis). Red = fraction of *rare* (tail) items still alive; green =
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fraction of *common* (head) items still alive. The red curve plunges far faster — rare knowledge
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is lost roughly an order of magnitude sooner than common knowledge.
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3. **Collapse.** Purple (left axis, log) = number of distinct items surviving, falling from 500
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toward ~1 (everything collapses onto a single dominant item). Orange (right axis) = forward-KL to
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truth, diverging as the tail vanishes.
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### Takeaway
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Unchecked model-on-model training is a ratchet: diversity decays on a precise mathematical schedule,
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and the rare tail is destroyed first. **Falsifier (not triggered):** if `H` had stayed flat, the
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whole thesis would fail. It didn't.
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@ -1,14 +0,0 @@
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|||
{
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||||
"experiment": "E1_reproduce_collapse",
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||||
"master_seed": 20260704,
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||||
"git_commit": "a6eb9b75124779375fa1a0b3a64115ecd705b218",
|
||||
"python": "3.14.5",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0"
|
||||
},
|
||||
"rows": 60100,
|
||||
"results_sha256": "038bf62046d593a61d0177f988f76897c9af5d9c370dc4d19bf37dc68d47afde"
|
||||
}
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||||
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@ -1,68 +0,0 @@
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experiment: E1_reproduce_collapse
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seed: 20260704
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n_replicates: 100
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source_config:
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experiment: E1_reproduce_collapse
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seed: 20260704
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n_replicates: 100
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generations: 600
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truth:
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K: 500
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R: 1
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tail: zipf
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zipf_s: 1.1
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tail_frac: 0.5
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tail_threshold: 0.001
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init: truth
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dynamics:
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n: 100
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teachers:
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K_T: 1
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rho: 0.0
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q: 1.0
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grounding:
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m: 0
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policy: proportional
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selection:
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mode: none
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novelty_alpha: 0.0
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remint:
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enabled: false
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period: null
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H_gate: null
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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output:
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dir: results/collapse_null
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grid:
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- label: {}
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lineage_cfg:
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truth:
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K: 500
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R: 1
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tail: zipf
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zipf_s: 1.1
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tail_frac: 0.5
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tail_threshold: 0.001
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init: truth
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dynamics:
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n: 100
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teachers:
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K_T: 1
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rho: 0.0
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q: 1.0
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grounding:
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m: 0
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policy: proportional
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selection:
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mode: none
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novelty_alpha: 0.0
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remint:
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enabled: false
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period: null
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H_gate: null
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generations: 600
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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@ -15,7 +15,7 @@ simulator, whose separate RNG would break that guarantee). Two landscapes:
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set of loci (each parent adaptive, neither carrying an incompatibility); a fraction `ρ` of
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cross-lineage locus pairs are BDMIs (penalty `s`), biting only when a hybrid inherits *both* derived
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alleles. Sweep divergence `d` (substitutions) for several `ρ`; `L=20`, 15 reps.
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- **NK** (`configs/inheritance/speciation_bdm_nk.yaml`): parents are *local optima* (hill-climbed) on a Kauffman NK
|
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- **NK** (`speciation_bdm_nk`, on the dev branch): parents are *local optima* (hill-climbed) on a Kauffman NK
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landscape; sweep ruggedness `K`. The emergent version.
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|
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### Results
|
||||
|
|
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Before Width: | Height: | Size: 368 KiB |
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@ -1,40 +0,0 @@
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|||
{
|
||||
"experiment": "llm_compose",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 366,
|
||||
"results_sha256": "92ea7965e13685f77a79b85393685a1e7b53e8c821778f2368c4a68097292818",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"dry",
|
||||
"grounded",
|
||||
"dry_cat"
|
||||
],
|
||||
"generations": 6,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 150,
|
||||
"n_gsm8k": 150,
|
||||
"n_mbpp": 100,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
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||||
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@ -1,49 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
generations: 6
|
||||
arms:
|
||||
- dry
|
||||
- grounded
|
||||
- dry_cat
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 16
|
||||
score_batch_size: 4
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
resume: true
|
||||
lora:
|
||||
r: 16
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||||
alpha: 32
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||||
output:
|
||||
dir: results/llm_compose/s1
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
- - 0.1
|
||||
- 0.9
|
||||
n_replicates: 1
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_compose",
|
||||
"master_seed": 2,
|
||||
"git_commit": null,
|
||||
"python": "3.11.7",
|
||||
"libraries": {
|
||||
"numpy": "2.4.6",
|
||||
"scipy": "1.17.1",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 122,
|
||||
"results_sha256": "fb747c86d164496f423a83ba09bbdf79eb85723fefbc3c2ccd28bba7c1f7086e",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"dry"
|
||||
],
|
||||
"generations": 6,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 150,
|
||||
"n_gsm8k": 150,
|
||||
"n_mbpp": 100,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
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@ -1,47 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 2
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 2
|
||||
generations: 6
|
||||
arms:
|
||||
- dry
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 32
|
||||
score_batch_size: 16
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
resume: true
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s2_dry
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
- - 0.1
|
||||
- 0.9
|
||||
n_replicates: 1
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_compose",
|
||||
"master_seed": 2,
|
||||
"git_commit": null,
|
||||
"python": "3.11.7",
|
||||
"libraries": {
|
||||
"numpy": "2.4.6",
|
||||
"scipy": "1.17.1",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 122,
|
||||
"results_sha256": "e0a1c452c8ae05f0830f97c3faf53530bda42e6420824dfb9e15a7b09399f11d",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"dry_cat"
|
||||
],
|
||||
"generations": 6,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 150,
|
||||
"n_gsm8k": 150,
|
||||
"n_mbpp": 100,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
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|
@ -1,47 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 2
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 2
|
||||
generations: 6
|
||||
arms:
|
||||
- dry_cat
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 32
|
||||
score_batch_size: 16
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
resume: true
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s2_dry_cat
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
- - 0.1
|
||||
- 0.9
|
||||
n_replicates: 1
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_compose",
|
||||
"master_seed": 2,
|
||||
"git_commit": null,
|
||||
"python": "3.11.7",
|
||||
"libraries": {
|
||||
"numpy": "2.4.6",
|
||||
"scipy": "1.17.1",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 122,
|
||||
"results_sha256": "a4355506daf356e216915d10e451ba314667e5e50e4b2e96ab38f0985d20e01b",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"grounded"
|
||||
],
|
||||
"generations": 6,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 150,
|
||||
"n_gsm8k": 150,
|
||||
"n_mbpp": 100,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
|
@ -1,47 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 2
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 2
|
||||
generations: 6
|
||||
arms:
|
||||
- grounded
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
@ -1,38 +0,0 @@
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||||
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Binary file not shown.
|
|
@ -1,47 +0,0 @@
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|||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dir: results/llm_compose/s3_dry
|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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Binary file not shown.
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|
@ -1,47 +0,0 @@
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|||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
dir: results/llm_compose/s3_dry_cat
|
||||
arm_ops:
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||||
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||||
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||||
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Binary file not shown.
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@ -1,47 +0,0 @@
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|||
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||||
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||||
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||||
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||||
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||||
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||||
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Before Width: | Height: | Size: 364 KiB |
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@ -1,49 +0,0 @@
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|||
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||||
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||||
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||||
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||||
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|
@ -1,47 +0,0 @@
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|||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
@ -1,47 +0,0 @@
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|||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s2_dry_cat
|
||||
arm_ops:
|
||||
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|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
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|
||||
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||||
|
|
@ -1,47 +0,0 @@
|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s2_grounded
|
||||
arm_ops:
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
|
|
@ -1,47 +0,0 @@
|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s3_dry
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
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|
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|
|
@ -1,47 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 3
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 3
|
||||
generations: 6
|
||||
arms:
|
||||
- dry_cat
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 32
|
||||
score_batch_size: 16
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
resume: true
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s3_dry_cat
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
- - 0.1
|
||||
- 0.9
|
||||
n_replicates: 1
|
||||
|
|
@ -1,47 +0,0 @@
|
|||
experiment: llm_compose
|
||||
seed: 3
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 3
|
||||
generations: 6
|
||||
arms:
|
||||
- grounded
|
||||
g: 0.1
|
||||
n_hard: 150
|
||||
n_gsm8k: 150
|
||||
n_mbpp: 100
|
||||
n_probe: 60
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 32
|
||||
score_batch_size: 16
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
resume: true
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose/s3_grounded
|
||||
arm_ops:
|
||||
dry: linear
|
||||
grounded: linear
|
||||
dry_cat: cat
|
||||
n_hard_val: 60
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
- - 0.1
|
||||
- 0.9
|
||||
n_replicates: 1
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_compose_gate",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 13,
|
||||
"results_sha256": "3c22a286420de512c6b3a114bb8d928072bc8750f861405252c8e563405d7b39",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"dry"
|
||||
],
|
||||
"generations": 0,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 150,
|
||||
"n_gsm8k": 100,
|
||||
"n_mbpp": 80,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
experiment: llm_compose_gate
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose_gate
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
generations: 0
|
||||
arms:
|
||||
- dry
|
||||
n_hard: 150
|
||||
n_gsm8k: 100
|
||||
n_mbpp: 80
|
||||
n_probe: 40
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
g: 0.1
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 16
|
||||
score_batch_size: 8
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose_gate
|
||||
n_replicates: 1
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_compose_gate",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 14,
|
||||
"results_sha256": "cc1c6f5ac13eda7c26eecb563b3fcd22b034027dfec432e80cb071a71bf2a7c6",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"compose": {
|
||||
"arms": [
|
||||
"dry"
|
||||
],
|
||||
"generations": 0,
|
||||
"g": 0.1,
|
||||
"k_inherit": 300,
|
||||
"conf_gate": 0.85,
|
||||
"epochs": 3,
|
||||
"n_hard": 120,
|
||||
"n_gsm8k": 100,
|
||||
"n_mbpp": 80,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
experiment: llm_compose_gate
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_compose_gate
|
||||
kind: llm_compose
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
generations: 0
|
||||
arms:
|
||||
- dry
|
||||
target: math500
|
||||
n_hard: 120
|
||||
n_hard_val: 50
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.2
|
||||
- 0.8
|
||||
n_gsm8k: 100
|
||||
n_mbpp: 80
|
||||
n_probe: 40
|
||||
k_inherit: 300
|
||||
epochs: 3
|
||||
conf_gate: 0.85
|
||||
g: 0.1
|
||||
spec_train: 1200
|
||||
spec_epochs: 3
|
||||
max_new_tokens: 320
|
||||
batch_size: 16
|
||||
score_batch_size: 8
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_compose_gate_math500
|
||||
n_replicates: 1
|
||||
|
|
@ -1,48 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_curriculum_g2",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 136,
|
||||
"results_sha256": "0ead4632190886ae91cefc2d95b8318a4330adc42d25c7cc8e5e5e422ea08275",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"curriculum": {
|
||||
"families": [
|
||||
"strings",
|
||||
"setops",
|
||||
"numtheory",
|
||||
"mixedtoken",
|
||||
"digits",
|
||||
"alphabet",
|
||||
"prime",
|
||||
"wordlen",
|
||||
"roman"
|
||||
],
|
||||
"lineages": 1,
|
||||
"generations": 9,
|
||||
"arms": [
|
||||
"isolated"
|
||||
],
|
||||
"baselines": [],
|
||||
"n_new": 300,
|
||||
"n_replay": 0,
|
||||
"operator": null,
|
||||
"ancestor_depth": null,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
|
@ -1,39 +0,0 @@
|
|||
experiment: llm_curriculum_g2
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_curriculum_g2
|
||||
kind: llm_curriculum
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
families:
|
||||
- strings
|
||||
- setops
|
||||
- numtheory
|
||||
- mixedtoken
|
||||
- digits
|
||||
- alphabet
|
||||
- prime
|
||||
- wordlen
|
||||
- roman
|
||||
lineages: 1
|
||||
generations: 9
|
||||
arms:
|
||||
- isolated
|
||||
baselines: []
|
||||
n_new: 300
|
||||
n_replay: 0
|
||||
n_test: 60
|
||||
n_val: 30
|
||||
epochs: 3
|
||||
lr: 0.0001
|
||||
max_new_tokens: 32
|
||||
batch_size: 32
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_curriculum_g2
|
||||
n_replicates: 1
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_curriculum_gate",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 144,
|
||||
"results_sha256": "c910942d8071f802cca5c01a8d50710622b4c921e4b8f5dfce67f19674016e8f",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"curriculum": {
|
||||
"families": [
|
||||
"strings",
|
||||
"setops",
|
||||
"numtheory"
|
||||
],
|
||||
"lineages": 3,
|
||||
"generations": 3,
|
||||
"arms": [
|
||||
"isolated",
|
||||
"society"
|
||||
],
|
||||
"baselines": [],
|
||||
"n_new": 300,
|
||||
"n_replay": 120,
|
||||
"operator": "linear",
|
||||
"ancestor_depth": null,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,42 +0,0 @@
|
|||
experiment: llm_curriculum_gate
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_curriculum_gate
|
||||
kind: llm_curriculum
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
families:
|
||||
- strings
|
||||
- setops
|
||||
- numtheory
|
||||
lineages: 3
|
||||
generations: 3
|
||||
arms:
|
||||
- isolated
|
||||
- society
|
||||
baselines: []
|
||||
n_new: 300
|
||||
n_replay: 120
|
||||
n_test: 60
|
||||
n_val: 30
|
||||
epochs: 3
|
||||
lr: 0.0001
|
||||
operator: linear
|
||||
merge_weights:
|
||||
- - 0.5
|
||||
- 0.5
|
||||
- - 0.3
|
||||
- 0.7
|
||||
- - 0.7
|
||||
- 0.3
|
||||
max_new_tokens: 32
|
||||
batch_size: 32
|
||||
train_batch_size: 2
|
||||
train_max_len: 448
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_curriculum_gate
|
||||
n_replicates: 1
|
||||
|
|
@ -1,50 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_curriculum_v5_calib",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 188,
|
||||
"results_sha256": "a585ee6a11a47ab65732abcd5e263b885a6cfa74fbcae0bec6b5c71bf2827fd8",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"curriculum": {
|
||||
"families": [
|
||||
"gsm8k",
|
||||
"mbpp",
|
||||
"boolq",
|
||||
"mnli",
|
||||
"sst2",
|
||||
"csqa",
|
||||
"arc",
|
||||
"winogrande",
|
||||
"squad",
|
||||
"nq_open",
|
||||
"hellaswag"
|
||||
],
|
||||
"lineages": 11,
|
||||
"generations": 1,
|
||||
"arms": [
|
||||
"isolated"
|
||||
],
|
||||
"baselines": [],
|
||||
"n_new": 300,
|
||||
"n_replay": 0,
|
||||
"operator": null,
|
||||
"ancestor_depth": null,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
|
@ -1,41 +0,0 @@
|
|||
experiment: llm_curriculum_v5_calib
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_curriculum_v5_calib
|
||||
kind: llm_curriculum
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
families:
|
||||
- gsm8k
|
||||
- mbpp
|
||||
- boolq
|
||||
- mnli
|
||||
- sst2
|
||||
- csqa
|
||||
- arc
|
||||
- winogrande
|
||||
- squad
|
||||
- nq_open
|
||||
- hellaswag
|
||||
lineages: 11
|
||||
generations: 1
|
||||
arms:
|
||||
- isolated
|
||||
baselines: []
|
||||
n_new: 300
|
||||
n_replay: 0
|
||||
n_test: 60
|
||||
n_val: 20
|
||||
epochs: 3
|
||||
lr: 0.0001
|
||||
max_new_tokens: 48
|
||||
batch_size: 24
|
||||
train_batch_size: 2
|
||||
train_max_len: 512
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_curriculum_v5_calib
|
||||
n_replicates: 1
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_curriculum_v5_calib_b",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 73,
|
||||
"results_sha256": "dfd9abe18530d6999399962bce0347f35e6e9151b09e87664ce9c94eddcbd0d7",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"curriculum": {
|
||||
"families": [
|
||||
"gsm8k",
|
||||
"mbpp",
|
||||
"sst2",
|
||||
"csqa",
|
||||
"winogrande",
|
||||
"nq_open"
|
||||
],
|
||||
"lineages": 6,
|
||||
"generations": 1,
|
||||
"arms": [
|
||||
"isolated"
|
||||
],
|
||||
"baselines": [],
|
||||
"n_new": 1200,
|
||||
"n_replay": 0,
|
||||
"operator": null,
|
||||
"ancestor_depth": null,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
|
@ -1,36 +0,0 @@
|
|||
experiment: llm_curriculum_v5_calib_b
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_curriculum_v5_calib_b
|
||||
kind: llm_curriculum
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
families:
|
||||
- gsm8k
|
||||
- mbpp
|
||||
- sst2
|
||||
- csqa
|
||||
- winogrande
|
||||
- nq_open
|
||||
lineages: 6
|
||||
generations: 1
|
||||
arms:
|
||||
- isolated
|
||||
baselines: []
|
||||
n_new: 1200
|
||||
n_replay: 0
|
||||
n_test: 60
|
||||
n_val: 20
|
||||
epochs: 3
|
||||
lr: 0.0001
|
||||
max_new_tokens: 48
|
||||
batch_size: 24
|
||||
train_batch_size: 2
|
||||
train_max_len: 512
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_curriculum_v5_calib_b
|
||||
n_replicates: 1
|
||||
Binary file not shown.
|
|
@ -5,7 +5,7 @@ families, Qwen2.5-1.5B, rank-16 adapters continued from the parent) but every li
|
|||
mnli, then diverges maximally, then converges, so mean pairwise complementarity by generation is
|
||||
0.00, 0.67, 0.70, 0.58, 0.33, 0.00 (Latin square: 1.00, 1.00, 0.80, 0.67, 0.33, 0.00). Arms:
|
||||
`isolated` (never merge) and `society` with `allow_veto: true` (declinable merge). Pre-registered in
|
||||
`tasks/prereg-llm-society-v4.md` §8h before running. Seed 1 local; seeds 2–3 on CX3
|
||||
`tasks/prereg-llm-society-v4.md on the dev branch` §8h before running. Seed 1 local; seeds 2–3 on CX3
|
||||
(`hpc/llm_curriculum_controls.pbs`). Layout: `results.parquet` (seed 1), `s2/`, `s3/`.
|
||||
|
||||
Why: in the Latin square, complementarity and generation number are collinear, so the veto's
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -1,45 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_curriculum_v5_g2",
|
||||
"master_seed": 1,
|
||||
"git_commit": "e4804adabcdce6d928c5e6b1e85429b2a6acf2fe",
|
||||
"python": "3.14.7",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.16.1",
|
||||
"peft": "0.20.0"
|
||||
},
|
||||
"rows": 73,
|
||||
"results_sha256": "27493600f7086998e911d87c98c5106eacec2092ff7ff9d6912844dd5c8b88bd",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-1.5B",
|
||||
"hard": false,
|
||||
"curriculum": {
|
||||
"families": [
|
||||
"mnli",
|
||||
"arc",
|
||||
"hellaswag",
|
||||
"squad",
|
||||
"boolq",
|
||||
"winogrande"
|
||||
],
|
||||
"lineages": 1,
|
||||
"generations": 6,
|
||||
"arms": [
|
||||
"isolated"
|
||||
],
|
||||
"baselines": [],
|
||||
"n_new": 300,
|
||||
"n_replay": 0,
|
||||
"operator": null,
|
||||
"ancestor_depth": null,
|
||||
"lora": {
|
||||
"r": 16,
|
||||
"alpha": 32
|
||||
}
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
|
@ -1,36 +0,0 @@
|
|||
experiment: llm_curriculum_v5_g2
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_curriculum_v5_g2
|
||||
kind: llm_curriculum
|
||||
base_model: Qwen/Qwen2.5-1.5B
|
||||
seed: 1
|
||||
families:
|
||||
- mnli
|
||||
- arc
|
||||
- hellaswag
|
||||
- squad
|
||||
- boolq
|
||||
- winogrande
|
||||
lineages: 1
|
||||
generations: 6
|
||||
arms:
|
||||
- isolated
|
||||
baselines: []
|
||||
n_new: 300
|
||||
n_replay: 0
|
||||
n_test: 60
|
||||
n_val: 20
|
||||
epochs: 3
|
||||
lr: 0.0001
|
||||
max_new_tokens: 48
|
||||
batch_size: 24
|
||||
train_batch_size: 2
|
||||
train_max_len: 512
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_curriculum_v5_g2
|
||||
n_replicates: 1
|
||||
|
|
@ -3,7 +3,7 @@
|
|||
Config `configs/llm/curriculum_v5_stop3.yaml`: the v5 `society` arm (three lineages, six real-dataset
|
||||
families in a Latin square, Qwen2.5-1.5B base, rank-16 adapters continued from the parent) with
|
||||
recombination switched off from generation 3 (`merge_until: 3`, no veto). Generations 0–2 merge
|
||||
obligately; 3–5 never. Pre-registered in `tasks/prereg-llm-society-v4.md` §8h before running.
|
||||
obligately; 3–5 never. Pre-registered in `tasks/prereg-llm-society-v4.md on the dev branch` §8h before running.
|
||||
Seed 1 local (batch 24 / train batch 2); seeds 2–3 on CX3 via `hpc/llm_curriculum_controls.pbs`
|
||||
(batch 48 / train batch 4), as in the v5 campaign. Layout: `results.parquet` (seed 1), `s2/`, `s3/`.
|
||||
|
||||
|
|
|
|||
|
|
@ -1,50 +0,0 @@
|
|||
# llm_directed — directed sex in weight space: breed offspring + select on the verifier (E10, 0.5B)
|
||||
|
||||
**Claim tested.** `llm_moe` left a clean gap: fusion can *compose* beyond the parents but the right
|
||||
blend is unknown and base-dependent, while pure routing is capped at the best parent. E10's answer is
|
||||
**directed sex** — biology can't preview offspring, an AI can: breed a *population* of recombinant
|
||||
offspring (the specialists merged at many different weights), score each against the verifier
|
||||
("reality") on a held-out validation split, and keep the fittest. Selection replaces betting on one
|
||||
a-priori blend. Two breeding objectives: best validation **overall**, and best validation
|
||||
**worst-family** (raw capability vs the balanced generalist).
|
||||
|
||||
**Setup.** Base **Qwen2.5-0.5B-Instruct**, the three cached `llm_merge` specialists, **16 offspring**
|
||||
(Dirichlet-weighted merges, concentration 0.5, pinning candidate 0 = uniform soup for reference),
|
||||
scored on an **80-task/family validation** split, winners reported on a **fresh 100-task/family test**
|
||||
split (no selection-on-test leakage). Seed 1.
|
||||
|
||||
### Results (test accuracy)
|
||||
| model | lists | strings | arith | overall | worst-family |
|
||||
|---|---|---|---|---|---|
|
||||
| best specialist (strings) | 0.08 | 1.00 | 0.80 | 0.63 | 0.08 |
|
||||
| merge_soup (uniform, candidate 0) | 0.26 | 0.74 | 0.91 | 0.64 | 0.26 |
|
||||
| **directed_overall** (bred for overall) | 0.17 | 0.99 | 0.92 | **0.69** | 0.17 |
|
||||
| **directed_balanced** (bred for worst-family) | 0.37 | 0.37 | 0.79 | 0.51 | **0.37** |
|
||||
|
||||
### What holds, and the honest cost
|
||||
- **Offspring selection beats the single a-priori blend — on the objective you breed for.**
|
||||
`directed_overall` reaches **0.69 overall > soup 0.64** (and > best parent 0.63); `directed_balanced`
|
||||
reaches **0.37 worst-family > soup 0.26**. Searching the recombination-weight space and letting the
|
||||
verifier choose beats committing to uniform averaging — the E10 "preview and keep the fittest" claim,
|
||||
in real weights.
|
||||
- **Single-objective selection trades off the other axis (honest).** Breeding for *overall* on
|
||||
lexically-imbalanced families finds a strings+arith-heavy blend that sacrifices the rare `lists`
|
||||
skill (0.17, below soup's 0.26); breeding for *balance* lifts worst-family to 0.37 but costs overall.
|
||||
Directed sex gives *control* over what you breed for — it does not hand you both for free.
|
||||
- **A global blend still trails per-input routing at a weak base.** At 0.5B the best directed *global*
|
||||
merge (0.69 / 0.43-max) does not beat `llm_moe`'s per-input **routing** (0.74 / 0.43): when the base
|
||||
is weak, adapting the recombination *per input* beats any one fixed blend, however well selected. So
|
||||
directed sex over blends beats *averaging*, not *routing* — combining the two (route, then select
|
||||
among routed+blended offspring) is the natural next operator.
|
||||
|
||||
### Takeaway
|
||||
Directed sex — breed a population, select on the verifier — is confirmed in real LLM weights: it beats
|
||||
the single uniform soup on whichever objective it optimises, the distinctly-AI advantage (offspring
|
||||
preview + unbounded candidates) that biology lacks. The honest scope at 0.5B: selection buys one axis
|
||||
at the other's expense, and a single global blend can't yet beat per-input routing. Whether searching
|
||||
blends + selection can exceed even the *strong* 7B soup (which routing could not) is answered by
|
||||
**`results/llm_directed_hpc/`: it can't — directed ≈ soup (0.868 ≈ 0.873)** because the 7B soup already
|
||||
composes to the ceiling on these near-saturated families, leaving no fitter offspring to breed. So
|
||||
directed sex helps exactly when the default blend is *suboptimal* (0.5B), and is inert when it is
|
||||
already near-optimal (7B). **Falsifier (not triggered at 0.5B):** directed offspring ≤ uniform soup on
|
||||
their bred objective — instead each beat it.
|
||||
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 130 KiB |
|
|
@ -1,25 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_directed",
|
||||
"master_seed": 1,
|
||||
"git_commit": "8da0dac00713fb9708804b4696a847a3767758d5",
|
||||
"python": "3.14.5",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.13.0",
|
||||
"peft": "0.19.1"
|
||||
},
|
||||
"rows": 35,
|
||||
"results_sha256": "143b86833cb320c7c6c693f0b5394f381a94a69277cc31ba5e0977e5ea6ffd78",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-0.5B-Instruct",
|
||||
"directed": {
|
||||
"n_candidates": 16,
|
||||
"concentration": 0.5,
|
||||
"n_val": 80
|
||||
}
|
||||
}
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
experiment: llm_directed
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_directed
|
||||
kind: llm_directed
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
base_model: Qwen/Qwen2.5-0.5B-Instruct
|
||||
families:
|
||||
- lists
|
||||
- strings
|
||||
- arith
|
||||
n_train: 700
|
||||
n_val: 80
|
||||
n_test: 100
|
||||
n_candidates: 16
|
||||
concentration: 0.5
|
||||
epochs: 3
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
output:
|
||||
dir: results/llm_directed
|
||||
Binary file not shown.
|
|
@ -1,48 +0,0 @@
|
|||
# llm_merge — recombining specialist LLMs (the first real-LLM prototype; blueprint C2/C4)
|
||||
|
||||
**Claim tested.** The first step from toy models toward real language models: does the sexual-
|
||||
reproduction result — recombining decorrelated specialists yields a model that exceeds/retains what
|
||||
any single parent has (E8) — appear in real LoRA-adapted LLM weights? This is a **prototype**, run
|
||||
on a single 16 GB consumer GPU, not the full society.
|
||||
|
||||
**Setup.** Base model **Qwen2.5-0.5B-Instruct** (Apache-2.0). Three *disjoint*, procedurally-generated
|
||||
task families with an **exact-match verifier** (the "reality that says no"): `lists` (list ops),
|
||||
`strings` (string ops), `arith` (integer arithmetic), deliberately made hard so specialists
|
||||
decorrelate. One **LoRA specialist** is fine-tuned per family (~90 s for all three), then the base,
|
||||
each specialist, and two weight-space **merges** — `soup` (averaged LoRA deltas) and `ties`
|
||||
(sign-reconciled union) — are evaluated on a held-out mixed test set. Seed 1, 100 test tasks/family.
|
||||
|
||||
### Results (accuracy)
|
||||
| model | lists | strings | arith | overall | **worst family** |
|
||||
|---|---|---|---|---|---|
|
||||
| base | 0.15 | 0.15 | 0.53 | 0.28 | 0.15 |
|
||||
| spec: lists | 0.43 | 0.16 | 0.71 | 0.43 | 0.16 |
|
||||
| spec: strings | 0.08 | **1.00** | 0.80 | 0.63 | 0.08 |
|
||||
| spec: arith | 0.11 | 0.22 | 0.78 | 0.37 | 0.11 |
|
||||
| **merge: soup** | 0.26 | 0.74 | 0.91 | 0.64 | **0.26** |
|
||||
| **merge: ties** | 0.23 | 0.71 | 0.90 | 0.61 | **0.23** |
|
||||
|
||||
### What holds, and what doesn't (honest)
|
||||
- **Strong and robust — balance / "retains all specialties".** The merges are the *only* models
|
||||
competent across **all** families: worst-family ≈ **0.25**, versus **< 0.16** for every single
|
||||
specialist (the best specialist, strings, is at 0.08 on its worst family). Each specialist spikes on
|
||||
its own family and is weak elsewhere; the merge is decent everywhere. This is the Fisher-Muller
|
||||
"a generalist assembled from specialists" signature, in real LLM weights.
|
||||
- **Marginal / noisy — "exceeds any parent overall".** On *overall* accuracy the merge only *matches*
|
||||
the best specialist (soup 0.64 vs strings-specialist 0.63; ties 0.61 is slightly below). At this
|
||||
scale (a 0.5 B model, 3 families, one seed) the strict "offspring exceed every parent" claim is not
|
||||
cleanly established.
|
||||
- **The dilution caveat, visible in the flesh.** On `lists`, the lists-specialist alone scores 0.43
|
||||
but the merge only 0.23–0.26 — weight-averaging *diluted* that specialist's contribution. This is
|
||||
exactly Layer-1's "merge, don't average" concern (E4) appearing in real weights; the finer
|
||||
soup-vs-ties advantage is not resolved at K=3.
|
||||
|
||||
### Takeaway
|
||||
The pipeline runs end-to-end on real LLMs on a 16 GB GPU (specialise → verify → merge → evaluate), and
|
||||
the **balance/retention** half of the sexual-reproduction claim reproduces clearly. The stronger
|
||||
"exceeds every parent" claim is marginal at this toy scale and is the thing a larger run should firm
|
||||
up — more, cleaner-decorrelated families; a bigger base; multiple seeds; and a merge that resists
|
||||
dilution (e.g. per-task-family weighting, or the offspring-selection of "directed sex"). That scaling
|
||||
is the natural HPC step; this prototype de-risks the machinery and shows the first sign in real
|
||||
weights. **Falsifier (partially triggered — reported honestly):** a single specialist matches the
|
||||
merge on *overall* here; the merge's advantage is currently specific to cross-family *balance*.
|
||||
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 116 KiB |
|
|
@ -1,20 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_merge",
|
||||
"master_seed": 1,
|
||||
"git_commit": "6bca1db61e1130ac6899308cc18e520cd9872839",
|
||||
"python": "3.14.5",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.13.0",
|
||||
"peft": "0.19.1"
|
||||
},
|
||||
"rows": 30,
|
||||
"results_sha256": "bbc13776970c9bc1e2779b1abe0ff5dbee85ef2e1380eba6fe6e5c9ba7e012aa",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-0.5B-Instruct"
|
||||
}
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
experiment: llm_merge
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
source_config:
|
||||
experiment: llm_merge
|
||||
kind: llm_merge
|
||||
seed: 1
|
||||
n_replicates: 1
|
||||
base_model: Qwen/Qwen2.5-0.5B-Instruct
|
||||
families:
|
||||
- lists
|
||||
- strings
|
||||
- arith
|
||||
n_train: 700
|
||||
n_test: 100
|
||||
epochs: 3
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
merges:
|
||||
- soup
|
||||
- ties
|
||||
output:
|
||||
dir: results/llm_merge
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,58 +0,0 @@
|
|||
# llm_moe — union-preserving recombination (route / max-merge) vs fusion (the real-weight E8 *max*)
|
||||
|
||||
**Claim tested.** E8 (analytic) found that the recombination *operator* matters: a **union** over
|
||||
parents (`max`) assembles a child fitter than any parent, while an **average** (`mean`, the model
|
||||
soup) conserves collapse. `llm_merge` showed fusion (soup/ties, which *average* the LoRA deltas). This
|
||||
experiment adds the **union** operator to real LLM weights — never average the parents, keep each
|
||||
specialist intact and **select** the right one per input (a Mixture-of-Experts *router*) or per module
|
||||
(winner-take-all) — and asks whether union beats fusion, and *where*.
|
||||
|
||||
**Setup.** Base **Qwen2.5-0.5B-Instruct**, reusing the three cached LoRA specialists from `llm_merge`
|
||||
(disjoint families `lists`/`strings`/`arith`, exact-match verifier), 100 test tasks/family, seed 1.
|
||||
Five recombination operators on the same test set:
|
||||
- **Fusion** (blend the deltas): `soup` = mean(Δₖ); `ties` = sign-reconciled union.
|
||||
- **Union** (never average): `route:oracle`/`route:learned` keep all adapters live and route each
|
||||
prompt to one specialist (MoE); `max-merge` builds one adapter taking, per module, the specialist
|
||||
with the largest-norm delta. The learned router is training-free — nearest-centroid over the *base*
|
||||
model's own prompt embeddings; its routing accuracy is reported.
|
||||
|
||||
### Results (accuracy)
|
||||
| operator | lists | strings | arith | overall | worst-family | router |
|
||||
|---|---|---|---|---|---|---|
|
||||
| base | 0.15 | 0.15 | 0.53 | 0.28 | 0.15 | — |
|
||||
| best specialist (strings) | 0.08 | 1.00 | 0.80 | 0.63 | 0.08 | — |
|
||||
| fuse: soup | 0.26 | 0.74 | 0.91 | 0.64 | 0.26 | — |
|
||||
| fuse: ties | 0.23 | 0.71 | 0.90 | 0.61 | 0.23 | — |
|
||||
| **route: oracle** | 0.43 | 1.00 | 0.78 | **0.74** | **0.43** | 1.00 |
|
||||
| **route: learned** | 0.43 | 1.00 | 0.78 | **0.74** | **0.43** | **1.00** |
|
||||
| max-merge | 0.18 | 0.34 | 0.87 | 0.46 | 0.18 | — |
|
||||
|
||||
### What holds
|
||||
- **Union (routing) beats fusion at a weak base — decisively.** Routing reaches **0.74 overall /
|
||||
0.43 worst-family**, above both fusion merges (soup 0.64/0.26) and every specialist. It recovers
|
||||
*each* specialist's own-family peak exactly (lists 0.43, strings 1.00, arith 0.78) because it *is*
|
||||
that specialist there — **no dilution**. This is E8's `max` (union) beating `mean` (average) in real
|
||||
LLM weights: exactly where fusion diluted the lists-specialist (0.43→0.26), routing keeps 0.43.
|
||||
- **The learned router is perfect here (1.00) — stated as a caveat, not a triumph.** The three families
|
||||
are lexically distinct, so nearest-centroid routing over base embeddings is trivially easy;
|
||||
`route:learned` equals `route:oracle`. Routing's advantage on *these* tasks therefore rests partly on
|
||||
the routing problem being easy — the honest scope. On overlapping/ambiguous skills the router would
|
||||
be the bottleneck, and that is the interesting failure mode to probe next.
|
||||
- **Static per-module `max-merge` is a poor union (0.46) — an informative negative.** Picking, per
|
||||
module, the largest-norm specialist delta is *not* input-adaptive: it collapses toward whichever
|
||||
specialist dominates the weight norms (arith 0.87, but lists 0.18, strings 0.34). The union benefit
|
||||
needs **routing** (input-adaptive selection), not weight-space surgery — "keep the parents whole"
|
||||
only pays off if you also *choose* the right parent per input.
|
||||
|
||||
### Takeaway
|
||||
Adds the union half of E8's operator dichotomy to real LLM weights and confirms its sign at a weak
|
||||
base: **route-don't-average > average**, with no dilution, mirroring the analytic `max > mean`. Two
|
||||
honest riders — the learned router is trivially good because the families are lexically separable, and
|
||||
the router-free `max-merge` union fails because it isn't input-adaptive. The regime question the 0.5B
|
||||
result raises — *does routing still help once a capable base lets fusion **compose** rather than
|
||||
dilute?* (`llm_merge_hpc` showed 7B soup already beats its specialists with no dilution) — is answered
|
||||
by **`results/llm_moe_hpc/`: the ordering flips.** At 7B fusion wins (soup 0.87 > routing 0.84),
|
||||
because routing is capped at the best parent while fusion composes beyond it. So "merge, don't average"
|
||||
is a **weak-base law**: union wins here (0.5B, dilution regime), fusion wins there (7B, composition
|
||||
regime). **Falsifier (not triggered at 0.5B):** fusion matching the routing ceiling, i.e. averaging
|
||||
never diluting.
|
||||
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|
Before Width: | Height: | Size: 124 KiB |
|
|
@ -1,27 +0,0 @@
|
|||
{
|
||||
"experiment": "llm_moe",
|
||||
"master_seed": 1,
|
||||
"git_commit": "585264d0b42f0e829229611bd83b08f5a5e418b7",
|
||||
"python": "3.14.5",
|
||||
"libraries": {
|
||||
"numpy": "2.5.0",
|
||||
"scipy": "1.18.0",
|
||||
"pandas": "3.0.3",
|
||||
"pyarrow": "24.0.0",
|
||||
"torch": "2.12.1",
|
||||
"transformers": "5.13.0",
|
||||
"peft": "0.19.1"
|
||||
},
|
||||
"rows": 47,
|
||||
"results_sha256": "3e73caaeae3b5d77ded3ba956af4b767c0d4025a41a7ae1841806ce74b78045e",
|
||||
"layer": "2",
|
||||
"tier": "llm",
|
||||
"base_model": "Qwen/Qwen2.5-0.5B-Instruct",
|
||||
"operators": [
|
||||
"soup",
|
||||
"ties",
|
||||
"moe_oracle",
|
||||
"moe_learned",
|
||||
"max_merge"
|
||||
]
|
||||
}
|
||||
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Reference in a new issue