# Introducing AutoJudge: Streamlined inference acceleration via automated dataset curation

DevFeed: [Introducing AutoJudge: Streamlined inference acceleration via automated dataset curation](<https://devfeed.tech/articles/introducing-autojudge-streamlined-inference-acceleration-via-automated-dataset-curation-80190.md>)

Original publisher: [Read original article](<https://www.together.ai/blog/introducing-autojudge-streamlined-inference-acceleration-via-automated-dataset-curation>)

Author: Roman Garipov, Fedor Velikonivtsev, Ivan Ermakov, Ruslan Svirschevski, Vage Egiazarian, Max Ryabinin

Published: 2025-12-03T00:00:00Z

Content type: article

Language: en

Sources: [Together.ai](<https://devfeed.tech/sources/together-ai.md>)

Topics: [vllm](<https://devfeed.tech/topics/vllm.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [LLM optimization](<https://devfeed.tech/topics/llm-optimization.md>), [Factuality scoring in AI](<https://devfeed.tech/topics/factuality-scoring-in-ai.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [llm-inference](<https://devfeed.tech/tags/llm-inference.md>), [speculative-decoding](<https://devfeed.tech/tags/speculative-decoding.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

AutoJudge speeds up large language model inference by accepting draft-token mismatches that do not affect task results. It uses a self-supervised classifier trained from existing embeddings to identify important mismatches, avoiding manual annotation. The article reports speedups across reasoning, programming, and bandwidth-limited tests, with accuracy trade-offs that depend on the task and threshold.

## Source excerpt

AutoJudge accelerates LLM inference by identifying which token mismatches actually matter. Using self-supervised learning to train a lightweight classifier, it accepts up to 40 draft tokens per cycle--delivering 1.5-2x speedups over standard speculative decoding with minimal accur