On-Policy Self-Distillation for Reasoning Compression

Hejian Sang, XYX, Zhengze Zhou, Ran He, Zhipeng Wang, Jiachen Sun

On-Policy Self-Distillation for Reasoning Compression: 9 upvotes on Hugging Face Daily Papers, #17 of 24 papers on 2026-03-06. Day-by-day upvote history.

Reasoning models think out loud, but much of what they say is noise. We introduce OPSDC (On-Policy Self-Distillation for Reasoning Compression), a method that teaches models to reason more concisely by distilling their own concise behavior back into themselves. The entire approach reduces to one idea: condition the same model on a "be concise" instruction to obtain teacher logits, and minimize per-token reverse KL on the student's own rollouts. No ground-truth answers, no token budgets, no difficulty estimators. Just self-distillation. Yet this simplicity belies surprising sophistication: OPSDC automatically compresses easy problems aggressively while preserving the deliberation needed for hard ones. On Qwen3-8B and Qwen3-14B, we achieve 57-59% token reduction on MATH-500 while improving accuracy by 9-16 points absolute. On AIME 2024, the 14B model gains 10 points with 41% compression. The secret? Much of what reasoning models produce is not just redundant-it is actively harmful, compounding errors with every unnecessary token.

Paper page on Hugging Face · arXiv

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