Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Add autoresearch run ledger: EXPERIMENTS/FINDINGS/leaderboard + raw train-eval logs
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Download run/notes/audit_code_report.md from AlexWortega/llm-cipher-reasoning: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
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https://huggingface.co/datasets/AlexWortega/llm-cipher-reasoning/resolve/main/run/notes/audit_code_report.md
- Command line
-
hf download hf://datasets/AlexWortega/llm-cipher-reasoning/run/notes/audit_code_report.md
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curl -L -o audit_code_report.md https://huggingface.co/datasets/AlexWortega/llm-cipher-reasoning/resolve/main/run/notes/audit_code_report.md
1.11 kB
audit-code report (2026-07-12) — digest
- Source-of-truth trainer: src/grpo_train_efficient.py (eff/eff3/mtword = same script, env-var diffs)
- Rewards eff-track: format 0.5 / correctness 2.0 / token_efficiency ramp 40→150 payout 1.5 (correct-gated) / mtword (Round D only) / logger w=0. reward_weights=[1,1,1,1,0]
- Eval: 5 parallel scripts, temp 0.3 top_p 0.9 max_new 512, seed-1 shuffle n=70; tokens = reasoning span only
- AIME eval: aime2026_eval_mtword.py, loads MathArena/aime_2026 from HF, max_new 2048, needs internet
- Round E chain: generate_corpus (400 GSM8K w/ eff3) → mine_supertokens (2-4-gram doc-freq, top-K 48, 238-example corpus in practice) → extend_vocab (add , mean-init embeddings) → sft_seed (r16 SFT, merge) → GRPO polish. Swap points: mining corpus, TOP_K/MIN_FREQ
- No requirements manifest; API scripts need OPENROUTER_API_KEY in ../.env (absent)
- GEPA side uses /usr/share/dict/words; train side uses data/dict_words.txt
- Extension points confirmed: reward shape :141-160, weights :221, SYSTEM_PROMPT :87-100 (must keep tags), max_completion_length :228