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home / papers / 2026 / Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models

Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models

Paper · 2026 · Hao Wang, Hao Gu, Hongming Piao et al.

Summary

Large reasoning models are usually post-trained by supervised fine-tuning followed by reinforcement learning (SFT-then-RL). The authors argue that cross-entropy SFT makes models overconfident and collapses token entropy, which leaves the RL stage little room to explore. Simply adding an entropy bonus does not solve this. It raises entropy everywhere, including on factual tokens, so it adds noise and causes forgetting instead of useful diversity. Their method, CurioSFT, has two parts:

  • Self-Exploratory Distillation pulls the model toward a softened version of its own output distribution, built by raising the temperature of an EMA-updated teacher.
  • Entropy-Guided Temperature Selection picks a temperature for each token. It boosts exploration on uncertain reasoning connectors such as "wait" and keeps confident factual tokens nearly unchanged.

On mathematical reasoning benchmarks, CurioSFT beats vanilla SFT on both in-distribution and out-of-distribution tasks. The diversity it preserves also leads to clearly larger improvements after RL. This suggests that how SFT is done strongly affects how much RL can add.

Key points

  • Compared with vanilla SFT, CurioSFT gains +2.5 points on in-distribution tasks and +2.9 on OOD tasks (GPQA, MMLU-Pro, ARC-C). After the RL stage, the average gain is 5.0 points.
  • Plain entropy loss is fragile. With α ≥ 0.08 it causes an "entropy explosion," and even α = 0.06 brings no Pass@32 gain on AIME 2024. Applying it only to the top-20% entropy tokens raises the OOD average from 51.3 to 52.3.
  • The key hyperparameter, the entropy pivot H_pivot, was swept from 0.4 to 1.4. Performance peaks near 1.2, and the method beats the baseline across the whole range.
  • Among the tested EMA teacher update intervals {1, 5, 10, 50}, updating every 5 steps works best, and n = 50 causes a sharp drop.
  • The authors note two limitations: an extra forward pass adds training cost, and gains depend on the base model's prior knowledge. Code is available at github.com/HaoooWang/CurioSFT.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hong Kong University of Science and TechnologyHong Kong · China142 works · 25,136 citations🎓Chinese University of Hong KongHong Kong · China126 works · 14,073 citations🎓City University of Hong KongHong Kong · China45 works · 3,014 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Hao Wang, Hao Gu, Hongming Piao, Kaixiong Gong, Yuxiao Ye, Xiangyu Yue, Sirui Han, Yike Guo and Dapeng Wu. 2026. Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.617

@inproceedings{wang2026learning,
  title = {{Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models}},
  author = {Hao Wang, Hao Gu, Hongming Piao, Kaixiong Gong, Yuxiao Ye, Xiangyu Yue, Sirui Han, Yike Guo and Dapeng Wu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.617},
  url = {https://doi.org/10.18653/v1/2026.acl-long.617}
}

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