Diagnosing On-Policy Self-Distillation for Reasoning Language Models
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Computer Science > Computation and Language
arXiv:2609.39118 (cs)
[Submitted on 30 Sep 2026]
Title:Diagnosing On-Policy Self-Distillation for Reasoning Language Models
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Abstract:On-policy self-distillation (OPSD) has attracted growing interest as a promising approach to improve the reasoning ability of language models. Without external rewards nor a separate stronger teacher, the self-teacher with privileged information could provide dense signals on student's trajectories. However, its behavior in language reasoning remains unclear, with reported outcomes ranging from modest gains to behavioral collapse. In this work, we diagnose OPSD for mathematical reasoning across models spanning 0.6B--8B parameters. We conduct controlled experiments and token-level analyses to fully delve into OPSD. We point out that teacher's signal is shaped by reasoning-mode alignment and the complete teacher prefix, rather than by privileged semantics alone. OPSD improves reasoning only in narrow compatibility regimes. Otherwise, it produces ineffective length growth, stable degradation, or behavioral collapse. Token-level analysis shows that teacher's signal is not stable and does not predict downstream performance. Based on these results, we argue that OPSD is a sensitive algorithm rather than a generally reliable reasoning-improvement post-training method.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.39118 [cs.CL] |
| (or arXiv:2609.39118v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39118
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Diagnosing On-Policy Self-Distillation for Reasoning Language Models, by Yang Li and 6 other authors
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