MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation

TL;DR

This paper introduces MCTS-Judge, a novel test-time scaling framework using Monte Carlo Tree Search to improve LLM-based code correctness evaluation, significantly enhancing accuracy and reasoning quality.

Contribution

It pioneers integrating test-time computation with MCTS in LLM-as-a-Judge, enabling more reliable reasoning in code evaluation tasks.

Findings

Accuracy improved from 41% to 80%.

Outperforms o1-series models with 3x fewer tokens.

Validates test-time scaling law for LLM-as-a-Judge.

Abstract

The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as programming. Inspired by recent advances in reasoning models and shifts in scaling laws, we pioneer bringing test-time computation into LLM-as-a-Judge, proposing MCTS-Judge, a resource-efficient, System-2 thinking framework for code correctness evaluation. MCTS-Judge leverages Monte Carlo Tree Search (MCTS) to decompose problems into simpler, multi-perspective evaluations. Through a node-selection strategy that combines self-assessment based on historical actions in the current trajectory and the Upper Confidence Bound for Trees based on prior rollouts, MCTS-Judge balances global optimization and refinement of the current trajectory. We further designed a high-precision, unit-test-level reward mechanism to encourage the Large Language Model (LLM) to perform line-by-line analysis. Extensive experiments on three benchmarks and five LLMs demonstrate the effectiveness of MCTS-Judge, which improves the base model's accuracy from 41% to 80%, surpassing the o1-series models with 3x fewer tokens. Further evaluations validate the superiority of its reasoning trajectory in logic, analytics, thoroughness, and overall quality, while revealing the test-time scaling law of the LLM-as-a-Judge paradigm.