JURIST: Training Small Data-Reweighted Reasoning Judges for Test-Time Scaling in Code Generation

Abstract

Best-of-N selection for code generation is only as good as the judge that picks among the candidates, and the judges accurate enough for this role are costly frontier LLMs. A small judge can be trained on abundant earlier data labeled by unit tests, but it is deployed on newer problems: the earlier problems are easier and teach it little, their tasks differ, and their candidates come from weaker policies, while data close to deployment are scarce. We present JURIST, a 1.7B reasoning judge that uses a small meta set close to deployment to learn how much each earlier training sample should count. In our experiments, we find that training on the meta set alone, or adding it to the training data, does not help, while using it to weight the earlier data through a bi-level objective adds 3.9 Hard points over adding it to the training data. On LiveCodeBench, JURIST reaches 84.7% Overall and matches Claude Sonnet 4.6 on Hard problems (73.8%) at 463x lower cost; on BigCodeBench, it surpasses all test-time scaling baselines (35.9%). Abundant earlier data, guided by a small deployment-aligned meta set, can thus train a compact judge that selects accurately at low cost.