Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning

• URL: http://arxiv.org/abs/2605.13255v1
• Date: Wed, 13 May 2026 09:38:20 GMT
• Title: Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
• Abstract summary: On-policy self-distillation trains a reasoning model on its own rollouts while a teacher provides dense token-level supervision.<n>We propose EGRSD (Entropy-Guided Reinforced Self-Distillation), which unifies token-level updates through three signals.<n> CL-EGRSD, a causal-lookratio variant that distinguishes sustained high-entropy spans from transient high-entropy positions.
• Score: 43.9367673156851
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provides dense token-level supervision. Existing objectives typically weight the teacher's token-level signal uniformly across a chain-of-thought sequence, despite substantial variation in the entropy of the teacher's predictive distribution. We propose EGRSD (Entropy-Guided Reinforced Self-Distillation), which unifies token-level updates through three signals: a reward-grounded direction, a teacher-student likelihood-ratio magnitude, and the proposed teacher-entropy confidence gate that down-weights high-entropy token positions while maintaining a nonzero lower bound on every token weight. We further introduce CL-EGRSD, a causal-lookahead variant that distinguishes sustained high-entropy spans from transient high-entropy positions whose following context rapidly becomes low entropy. Experiments with Qwen3-4B and Qwen3-8B in thinking mode show that EGRSD and CL-EGRSD advance the accuracy-length frontier among the compared trainable methods.

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