arXiv:2605.22263v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is an emerging LLM post-training paradigm in which the model serves as its own teacher: conditioned on privileged information such as a reference trace or hint, the same policy provides dense token-level supervision on its own rollouts. However, recent studies show that OPSD degrades complex reasoning by suppressing predictive uncertainty, which supports exploration and hypothesis revision. Our token-level analysis shows that this failure arises from applying a uniform direction of teacher supervision across tok
The paper addresses a known limitation in current LLM self-distillation techniques, which is becoming critical as models scale and are applied to complex reasoning tasks.
Improving LLM reasoning capabilities directly impacts the potential for more robust and reliable AI systems, especially for general-purpose applications.
The proposed 'direction-adaptive self-distillation' method suggests a pathway to overcome issues with existing self-distillation, potentially leading to more effective and less error-prone LLMs for reasoning.
- · AI research labs
- · Developers of LLM-powered applications
- · Sectors requiring complex AI reasoning (e.g., finance, healthcare)
- · Developers of less robust, uncertainty-suppressing LLMs
Improved LLM reasoning leads to more accurate and reliable outputs for complex problems.
Enhanced reasoning could accelerate the development of more autonomous and capable AI agents.
More sophisticated AI agents might displace a wider range of white-collar tasks, impacting labor markets.
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Read at arXiv cs.LG