What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training
Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introduce desired reasoning strategies and behavioral patterns. Yet forcing a model to imitate externally provided trajectories may also interfere with behaviors already supported by pretraining, limiting generalization beyond the training distribution. Recent dynamic SFT methods mitigate this issue by reweighting token-level supervision according to model-side signals such as predictive probability, prior support, or distributional compatibility. However, model compatibility is not equivalent to learning value: a token that is weakly supported by ...
| Authors | Yuxuan Gu, Kequan Niu, Xiaocheng Feng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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