Beyond Imitation: Reflective On-Policy Self-Distillation for LLM Reasoning
On-policy self-distillation (OPSD) improves the reasoning capabilities of large language models (LLMs) by providing dense token-level supervision for on-policy rollouts. However, existing OPSD methods often yield limited gains on complex reasoning tasks and suffer from severe training instability. We identify two key causes: conditioning the self-teacher on a complete verified solution encourages imitation of complete reference trajectories rather than extraction of transferable reasoning insights, while indiscriminate full-response distillation imposes superfluous supervision on already-valid reasoning prefixes. Together, these issues suppress reasoning diversity and contribute to late-s...
| Authors | Ziqi Zhao, Xinyu Ma, Liu Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 28 September 2026 |
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