| --- |
| pretty_name: AdaptiveSFT baseline runs |
| --- |
| |
| # AdaptiveSFT: baseline SFT-loss runs |
|
|
| Full fine-tuning runs of seven token-level SFT objectives (and the untrained base model) swept over learning rates, with their checkpoints, training logs, sampled eval outputs and scores, plus the exact training and eval sets. Code: https://github.com/jiarui-liu/adaptive-reasoning-loss (framework `arl/`, the commit of each run is in the tables). |
|
|
| ## Layout |
|
|
| ``` |
| data/sft/v1/<set>.jsonl training sets ({prompt, response[, source]}) |
| data/sft/v1/eval_sets/<bench>.jsonl eval sets ({problem, answer, kind, ...}) |
| runs/<grid>/<method>_lr<lr>/ |
| final/ checkpoint (safetensors + tokenizer), loads with transformers / vLLM |
| args.json all training arguments |
| train_log.jsonl one row per optimizer step: loss, nll, grad norm, lr, tokens, method statistics |
| train.log, eval.log raw stdout of training / evaluation |
| train.sbatch, eval.sbatch the exact slurm scripts that produced the run |
| eval/<bench>.jsonl every sample per eval problem, finish reasons, token counts |
| eval/scores.json pass@1 / pass@k, mean length, truncation rate per bench |
| ``` |
|
|
| `base` rows evaluate the untrained starting model (no `final/`). Data builders: `experiments/data/` (`build_sets.py` for the math sets, `build_tulu.py` for the Tulu subset and IFEval / GSM8K / MMLU / HumanEval+). Prompt formats: math sets use `Problem: {prompt}\n\nSolution:\n`; tulu3_50k uses `<|user|>\n{prompt}\n<|assistant|>\n` (`arl/data.py`). |
| |
| ## Methods |
| |
| | method | per-token objective | |
| |---|---| |
| | nll | standard cross-entropy | |
| | dft | NLL weighted by the model's own (stop-gradient) probability of the target token | |
| | focal | focal loss, down-weights tokens the model is already confident on | |
| | trimsft | NLL with part of the tokens trimmed from the loss | |
| | prift | weights from the frozen starting model's probabilities (mode `mass`) | |
| | asft | DFT weighting plus a KL anchor to the frozen starting model | |
| | psft | PPO-style clipped ratio against the model a few steps ago (reference refreshed every 4 steps) | |
| |
| Scores are pass@1 in % (mean over samples). Runs appear here once their evaluation has finished. |
| |
| ## s1_numina_q17 |
| |
| math reasoning distillation: Qwen3-1.7B-Base on numina_cot_20k (20k NuminaMath-CoT problems, one CoT solution each), 3 epochs, 64 seqs/step, max 4096 tokens; eval on math benchmarks |
| |
| | run | method | model | lr | method args | aime24 | aime25 | amc23 | math500 | minerva | olympiadbench | code commit | |
| |---|---|---|---|---|---|---|---|---|---|---|---| |
| | `nll_lr5e-6` | nll | Qwen3-1.7B-Base | 5e-06 | `{}` | 1.7 | 1.9 | 26.6 | 52.5 | 17.9 | 22.6 | `28b0306 (inferred from start time)` | |
| | `nll_lr1e-5` | nll | Qwen3-1.7B-Base | 1e-05 | `{}` | 1.7 | 1.0 | 27.0 | 52.8 | 17.6 | 22.4 | `28b0306 (inferred from start time)` | |
| | `nll_lr2e-5` | nll | Qwen3-1.7B-Base | 2e-05 | `{}` | 1.0 | 2.1 | 26.9 | 50.0 | 16.5 | 21.7 | `28b0306 (inferred from start time)` | |
| | `nll_lr5e-5` | nll | Qwen3-1.7B-Base | 5e-05 | `{}` | 1.0 | 0.8 | 22.8 | 47.0 | 16.2 | 17.8 | `28b0306 (inferred from start time)` | |
|
|
| ## Sources and licenses |
|
|
| Base model: Qwen/Qwen3-1.7B-Base (Apache-2.0). Training data are subsets of AI-MO/NuminaMath-CoT, open-r1/OpenR1-Math-220k and allenai/tulu-3-sft-mixture (ODC-BY; sub-sources keep their own licenses). Eval sets: MATH-500, Minerva Math, OlympiadBench, AMC23, AIME24/25, IFEval, GSM8K, MMLU, HumanEval+. |
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