arXiv:2605.28014v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) improves the reasoning performance of large language models (LLMs) by providing dense token-level supervision for on-policy rollouts. However, existing OPSD methods often yield limited gains on in-domain reasoning and generalize poorly to out-of-domain problems. We identify two key causes: conditioning the self-teacher on a verified solution encourages imitation of training-domain reference trajectories rather than error-specific correction, and applying distillation to the full response can overwrite valid re
The paper addresses current limitations in large language model reasoning, specifically the 'imitation of training-domain' and poor generalization to out-of-domain problems, which are active research areas in AI development.
Improving LLM reasoning and generalization across domains is critical for their wider applicability and robustness in real-world scenarios, directly impacting the utility and trustworthiness of AI systems.
This research outlines a methodology for more effective self-distillation, which could lead to LLMs that are not only better at in-domain tasks but also more adaptive to novel challenges.
- · AI researchers
- · LLM developers
- · AI-powered industries
- · Models with poor generalization
- · Companies relying on narrow AI applications
Reflective on-policy self-distillation will enhance the reasoning capabilities and domain transfer of large language models.
Improved LLM reasoning will accelerate the development of more capable AI agents and intelligent systems able to operate across diverse problem sets.
Enhanced AI reasoning could lead to the automation of more complex white-collar tasks, further impacting professional workflows and the SaaS landscape.
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