aiqbench analysis
This research introduces Reflective On-policy Self-Distillation (ROSD), a novel training technique designed to enhance the reasoning capabilities of large language models by focusing on extracting transferable insights rather than imitating complete solution trajectories. By masking valid prefixes and applying targeted token-level supervision only after identified errors, ROSD effectively mitigates training instability and mode collapse while preserving reasoning diversity. The method demonstrates consistent performance gains across multiple benchmarks compared to standard self-distillation and reinforcement learning baselines.
Beyond Imitation: Reflective On-Policy Self-Distillation for LLM Reasoning
arXiv:2605.28014v2 Announce Type: replace-cross Abstract: 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-stage mode collapse. We propose Reflective On-policy Self-Distillation (ROSD), which distills transferable reasoning insights rather than complete reference trajectories. For each erroneous rollout, a self-reflector contrasts it with a correct rollout from the same group to derive a corrective idea and identify the sentence containing the first reasoning error. The corrective idea provides the self-teacher with targeted guidance, while the diagnosed error boundary allows ROSD to mask out the distillation loss over the valid prefix and apply token-level distillation only from the first erroneous sentence onward. Experiments across multiple reasoning benchmarks and model backbones show that ROSD consistently outperforms standard OPSD and reinforcement learning baselines, better preserves reasoning diversity, stabilizes training, and mitigates late-stage mode collapse. Code is available at https://github.com/ZiqiZhao1/ROSD.
Abstract: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-stage mode collapse. We propose Reflective On-policy Self-Distillation (ROSD), which distills transferable reasoning insights rather than complete reference trajectories. For each erroneous rollout, a self-reflector contrasts it with a correct rollout from the same group to derive a corrective idea and identify the sentence containing the first reasoning error. The corrective idea provides the self-teacher with targeted guidance, while the diagnosed error boundary allows ROSD to mask out the distillation loss over the valid prefix and apply token-level distillation only from the first erroneous sentence onward. Experiments across multiple reasoning benchmarks and model backbones show that ROSD consistently outperforms standard OPSD and reinforcement learning baselines, better preserves reasoning diversity, stabilizes training, and mitigates late-stage mode collapse. Code is available at this https URL.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2605.28014 [cs.CL]
(or arXiv:2605.28014v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28014 Focus to learn more arXiv-issued DOI via DataCite
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