Giorgio Gilestro
5a23ddaf2a
llm_speciation (new kind; src/llm/speciation.py): E13 in LLM weights. LoRA children share the frozen base's coordinates, so merge failure is functional by construction. CONFLICT (ambiguous sort prompts learned under opposite conventions — the BDM structure): function-specific hybrid breakdown — merged coherence 0.02-0.08 falls below BOTH parents (~0.2) on the conflicted function; and in the de-confounded `add` design (private budget fixed, conflict added on top; 3 seeds after a single-seed pilot showed one anomalous point) the merge's private-family accuracy shows NO trend with conflict — the damage is surgical, not global. DURATION (over-trained disjoint specialists, 1->12 epochs): the merge improves (0.84->0.94) and stays above the best parent — the MLP "no emergent isolation" null generalises; relevant to the expert-training-duration report (2607.11997), with the epistasis prediction left to the decisive experiment. Multi-seed firm-up (seeds threaded into specialist caches; `seeds:` list support in the runner; fixed test sets): all three recombination claims hold with CIs — merges beat every specialist (5 seeds, ties 0.647±0.027 > best spec 0.592±0.009; worst-family 0.28 vs <=0.16); union 0.274±0.026 > fusion 0.174±0.102 on hard (3 seeds); directed 0.221±0.026 > soup. NEW finding: fusion is seed-FRAGILE where headroom exists (CI ±0.10) while routing/directed selection are stable (±0.026) — the union/selection operators win on reliability, not just mean. Figures (llm_speciation 3-panel; llm_seeds 3-panel with 95% CI), READMEs, +1 convention test (150 green), make llm-speciation / llm-seeds targets. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
20 lines
630 B
YAML
20 lines
630 B
YAML
experiment: llm_merge_seeds
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kind: llm_merge
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seed: 1
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seeds: [1, 2, 3, 4, 5]
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n_replicates: 1
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# Multi-seed firm-up of the 0.5B merge experiment (PNAS work order Phase 3: removes the "one seed"
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# objection). Same protocol as configs/llm/merge.yaml; the test sets stay FIXED (seed 1000+i per
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# family) while the training seed varies, so across-seed variance is training variance only.
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# Specialists cache per-seed (spec_<family>_s<seed>).
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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families: [lists, strings, arith]
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n_train: 600
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n_test: 100
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epochs: 3
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lora: {r: 16, alpha: 32}
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merges: [soup, ties]
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output: {dir: results/llm_merge_seeds}
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