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+.