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
27 lines
1.2 KiB
YAML
27 lines
1.2 KiB
YAML
# Calibration for the real-dataset curriculum (prereg v4 §5 gates, re-run on the new families).
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# Stage A (this config): base accuracy on every candidate, and one specialist per candidate trained
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# from the base on its train split — the C1 band is base <= 0.40 and specialist >= 0.60.
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# Stage B (curriculum_v5_g2.yaml, after selection): one lineage, zero replay, all chosen families in
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# sequence — the forgetting probe; mean drop across families learned before the last must be >= 0.15,
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# and it must not be carried by a single family (the v4 failure: one pair at +0.65, the rest ~0).
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experiment: llm_curriculum_v5_calib
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kind: llm_curriculum
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base_model: Qwen/Qwen2.5-1.5B
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seed: 1
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families: [gsm8k, mbpp, boolq, mnli, sst2, csqa, arc, winogrande, squad, nq_open, hellaswag]
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lineages: 11 # one lineage per candidate = each trains only its own family at gen 0
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generations: 1 # gen 0 only: founders; the recorded acc_* rows ARE the specialist table
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arms: [isolated]
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baselines: []
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n_new: 300
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n_replay: 0
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n_test: 60
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n_val: 20
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epochs: 3
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lr: 1.0e-4
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max_new_tokens: 48 # spans / short text need more room than a label
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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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lora: {r: 16, alpha: 32}
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output: {dir: results/llm_curriculum_v5_calib}
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