LLM-as-a-Verifier: A probabilistic verification framework for scaling LLM correctness assessment

[Submitted on 6 Jul 2026]

4h ago· 2 min readenInsight

Summary

This paper introduces LLM-as-a-Verifier, a general-purpose verification framework that uses LLMs to determine the correctness of solutions in agentic tasks without requiring additional training. Unlike standard LM judges that produce discrete scores, this framework computes continuous scores by taking expectations over scoring token logits, enabling verification to scale along three dimensions: score granularity, repeated evaluation, and criteria decomposition. The approach achieves state-of-the-art performance on multiple benchmarks including Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%). The framework also provides fine-grained signals that can serve as a proxy for estimating task progress and can provide dense feedback for reinforcement learning, improving sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.

Source

Twitter / XLLM-as-a-Verifier: A probabilistic verification framework for scaling LLM correctness assessmentarxiv.org

Key quotes

· 5 pulled

We identify verification, the ability to determine the correctness of a solution, as a new scaling axis.

Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores.

LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%).

Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress.

We show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.

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Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To

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