Title: SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification
Primary area: generative models
Keywords: Large Language Model, Speculative Decoding, Efficient Inference

Abstract: Speculative decoding accelerates LLM inference by verifying candidate tokens from a draft model against a larger target model. Recent "judge'' decoding boosts this process by relaxing verification criteria by accepting draft tokens that may exhibit minor discrepancies from target model output, but existing methods are restricted by their reliance on human annotations or tasks with verifiable ground truths, limiting generalizability across diverse NLP tasks. We propose SelfJudge, which trains judge verifiers via self-supervision of the target model. Our method measures semantic preservation by assessing whether token-substituted responses preserve the meaning of original responses, enabling automatic verifier training across diverse NLP tasks. Our experiments show SelfJudge achieves superior inference-accuracy trade-offs than judge decoding baselines, offering a broadly applicable solution for faster LLM inference.

TL;DR: Faster speculative decoding based on judge verification. Our judge verifier is trained through the target model's self-evaluation, which enables generalizing judge verification across diverse NLP tasks.