Introducing AutoJudge: Streamlined Inference Acceleration via Automated Dataset Curation
Published:Dec 3, 2025 00:00
•1 min read
•Together AI
Analysis
The article introduces AutoJudge, a method for accelerating Large Language Model (LLM) inference. It focuses on identifying critical token mismatches to improve speed. AutoJudge employs self-supervised learning to train a lightweight classifier, processing up to 40 draft tokens per cycle. The key benefit is a 1.5-2x speedup compared to standard speculative decoding, while maintaining minimal accuracy loss. This approach highlights a practical solution for optimizing LLM performance, addressing the computational demands of these models.
Key Takeaways
Reference
“AutoJudge accelerates LLM inference by identifying which token mismatches actually matter.”