Judge Decoding introduces a novel approach to accelerate LLM inference by separating correctness verification from target model alignment.

• Achieves 2.5-4.5x faster inference compared to traditional autoregressive generation
• Introduces specialized judge models that verify token candidates more efficiently than the target model
• Demonstrates that verification doesn't require full alignment with the target model, challenging previous assumptions
• Provides a practical framework for implementing accelerated inference in production LLM systems

This engineering breakthrough matters because it directly addresses one of the most critical bottlenecks in LLM deployment: generation speed. By decoupling verification from alignment, organizations can deploy faster language models without sacrificing output quality.

Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment