Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence

• URL: http://arxiv.org/abs/2601.04766v1
• Date: Thu, 08 Jan 2026 09:34:54 GMT
• Title: Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence
• Authors: Shengyin Sun, Yiming Li, Renxi Liu, Weizhe Lin, Hui-Ling Zhen, Xianzhi Yu, Mingxuan Yuan, Chen Ma,
• Abstract summary: Judge Decoding accelerates inference by relaxing the strict verification of Speculative Decoding.<n>In this work, we revisit this paradigm from first principles, revealing that the criticality'' scores learned via costly supervision are intrinsically encoded in the draft-target distributional divergence.
• Score: 31.435770434219005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Judge Decoding accelerates LLM inference by relaxing the strict verification of Speculative Decoding, yet it typically relies on expensive and noisy supervision. In this work, we revisit this paradigm from first principles, revealing that the ``criticality'' scores learned via costly supervision are intrinsically encoded in the draft-target distributional divergence. We theoretically prove a structural correspondence between learned linear judges and Kullback-Leibler (KL) divergence, demonstrating they rely on the same underlying logit primitives. Guided by this, we propose a simple, training-free verification mechanism based on KL divergence. Extensive experiments across reasoning and coding benchmarks show that our method matches or outperforms complex trained judges (e.g., AutoJudge), offering superior robustness to domain shifts and eliminating the supervision bottleneck entirely.

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