arXiv:2607.02234v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-level supervision on the student's own generated trajectories. However, we find that OPSD consistently fails on long chain-of-thought (long-CoT) reasoning models, yielding at best marginal gains while destabilizing the reflective reasoning capability these models depend on. Through a novel decomposition of the teacher's supervision signal, we identify the root cause: the
This paper addresses a critical limitation in on-policy self-distillation (OPSD) for large language models, a technique currently seen as promising for improving reasoning abilities.
Improving LLM reasoning is central to unlocking more advanced AI capabilities, and understanding the failure modes of current training paradigms is crucial for future development.
The identified root cause for OPSD's failure in long-CoT reasoning models implies a necessary re-evaluation of current self-distillation strategies for advanced LLM training.
- · AI researchers
- · LLM developers focusing on robust reasoning
- · Teams overly reliant on current OPSD for complex reasoning improvements
Further research and development will focus on refining or replacing OPSD techniques for long chain-of-thought reasoning.
New architectures or training methodologies may emerge to more effectively imbue LLMs with reflective and stable reasoning capabilities.
The overall timeline for highly robust, general-purpose AI agents capable of complex, reflective thought might be subtly extended or fundamentally re-routed.
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