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    <abstract>Reinforcement learning from verifiable rewards improves the reasoning ability of large language models, but often suffers from entropy collapse, in which increasingly concentrated policies reduce rollout diversity and useful learning signals. Existing remedies either constrain the RL objective (e.g., entropy regularization) or adjust sampling temperature during rollout collection, but these interventions remain external to the model parameters. We propose Temperature-Scaled On-Policy Self-Distillation (TS-OPSD), a lightweight policy reheating method that internalizes the exploratory effect of temperature into model parameters. Starting from an entropy-collapsed RL checkpoint, TS-OPSD constructs a self-teacher by applying high-temperature scaling to the model’s own logits, then distills the resulting smoother distribution back into the student. This policy reheating requires no external teacher, privileged data, or additional inference cost. Experiments on Qwen3-4B-Base and Qwen3-8B-Base show that policy reheating yields a stronger initialization for continued RL than both standard continued RL and rollout-level temperature reheating. Further analyses show that TS-OPSD mainly reduces output sharpness while preserving intermediate representations, top candidate sets, and reasoning capability. These results suggest that entropy restoration can serve as a simple post-collapse intervention for extending reasoning-oriented RL.</abstract>
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%0 Conference Proceedings
%T Internalize the Temperature: On-Policy Self-Distillation as Policy Reheater for Reinforcement Learning
%A Yang, Xuewei
%A Yu, Jiachen
%A Wu, Jie
%A Sun, Shaoning
%A Wang, Junjie
%A Yang, Yujiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-internalize
%X Reinforcement learning from verifiable rewards improves the reasoning ability of large language models, but often suffers from entropy collapse, in which increasingly concentrated policies reduce rollout diversity and useful learning signals. Existing remedies either constrain the RL objective (e.g., entropy regularization) or adjust sampling temperature during rollout collection, but these interventions remain external to the model parameters. We propose Temperature-Scaled On-Policy Self-Distillation (TS-OPSD), a lightweight policy reheating method that internalizes the exploratory effect of temperature into model parameters. Starting from an entropy-collapsed RL checkpoint, TS-OPSD constructs a self-teacher by applying high-temperature scaling to the model’s own logits, then distills the resulting smoother distribution back into the student. This policy reheating requires no external teacher, privileged data, or additional inference cost. Experiments on Qwen3-4B-Base and Qwen3-8B-Base show that policy reheating yields a stronger initialization for continued RL than both standard continued RL and rollout-level temperature reheating. Further analyses show that TS-OPSD mainly reduces output sharpness while preserving intermediate representations, top candidate sets, and reasoning capability. These results suggest that entropy restoration can serve as a simple post-collapse intervention for extending reasoning-oriented RL.
%U https://aclanthology.org/2026.findings-emnlp.1727/
%P 36939-36956

Markdown (Informal)

[Internalize the Temperature: On-Policy Self-Distillation as Policy Reheater for Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.1727/) (Yang et al., Findings 2026)

• Internalize the Temperature: On-Policy Self-Distillation as Policy Reheater for Reinforcement Learning (Yang et al., Findings 2026)

ACL

• Xuewei Yang, Jiachen Yu, Jie Wu, Shaoning Sun, Junjie Wang, and Yujiu Yang. 2026. Internalize the Temperature: On-Policy Self-Distillation as Policy Reheater for Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 36939–36956, Budapest, Hungary. Association for Computational Linguistics.