Giorgio Gilestro
84124de143
Clarity pass over the main text (36-item audit), Discussion rewrite and cut, acknowledgements, Souly et al. as ref 62, lettered SI panels, model section moved under Results; plus the untracked curriculum/society/compose/smol configs, runners, figures, stats and tests that the SI already cites. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
37 lines
2 KiB
YAML
37 lines
2 KiB
YAML
# The curriculum society campaign, seed 1 (prereg v4, v5 curriculum — tasks/prereg-llm-society-v4.md).
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# Runs all four arms then the three matched-budget baselines. The PBS array (hpc/llm_curriculum.pbs)
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# derives one (seed, arm) config per element from this template for seeds 2-3.
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#
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# Six real-dataset families selected by calibration (§8a): five pass C1 at >= 0.60, winogrande (0.57)
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# is the sixth under the recorded amendment. L = 3 lineages, G = 6 generations; the cyclic Latin square
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# gives complementarity 1.0 at generation 2 and 0.0 at generation 6 (H6 tests the shape).
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#
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# Arms: isolated (no recombination) · society (decorrelated contemporary) · society_dry (contemporary,
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# self-generated replay) · seed_bank (own ancestor at t-3 — temporal complementarity)
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# Baselines at matched examples: sequential · single_shot_merge · joint
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experiment: llm_curriculum_v5
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kind: llm_curriculum
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base_model: Qwen/Qwen2.5-1.5B # base, not Instruct: measured 0.011 on these families untrained
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seed: 1
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families: [mnli, arc, hellaswag, squad, boolq, winogrande]
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lineages: 3
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generations: 6
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arms: [isolated, society, society_dry, seed_bank]
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baselines: [sequential, single_shot_merge, joint]
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n_new: 300 # founder/acquisition budget that passed C1 (1200 destabilised training)
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n_replay: 150 # fixed TOTAL, split across families seen: protection thins as the curriculum grows
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n_test: 60 # per family, from the TEST split — reporting only
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n_val: 20 # per family, from the TRAIN split — merge-weight selection only
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epochs: 3
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lr: 1.0e-4 # continued-training rate (fresh founders use train_lora_on_tasks' 2e-4)
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ancestor_depth: 3
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operator: linear
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merge_weights: [[0.5, 0.5], [0.3, 0.7], [0.7, 0.3]]
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baseline_weights: [[0.333, 0.333, 0.334], [0.5, 0.25, 0.25], [0.25, 0.5, 0.25], [0.25, 0.25, 0.5]]
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max_new_tokens: 48
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batch_size: 24
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train_batch_size: 2
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train_max_len: 512
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resume: true
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lora: {r: 16, alpha: 32}
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output: {dir: results/llm_curriculum_v5/s1}
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