DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation

• URL: http://arxiv.org/abs/2601.22230v1
• Date: Thu, 29 Jan 2026 19:04:24 GMT
• Title: DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
• Authors: Peijia Qin, Ruiyi Zhang, Qi Cao, Pengtao Xie,
• Abstract summary: We propose DAJ, a reasoning-based LLM judge trained with rewards under a bi-level data-reweighted learning framework.<n>Our approach automatically emphasizes hard problems, in-distribution samples, and trajectory-aligned data, without relying on hand-crafted verifiables.
• Score: 30.131052926559956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time scaling for code generation commonly relies on Best-of-N selection, in which multiple candidate solutions are sampled from a base model, and the best one is selected by an LLM judge. However, training reliable LLM judges is challenging due to severe distribution shifts, including imbalances between easy and hard problems, mismatches between training tasks and evaluation benchmarks, and trajectory mismatch arising from training data generated by cheaper models whose behavior differs from that of inference-time models. We propose DAJ, a reasoning-based LLM judge trained with verifiable rewards under a bi-level data-reweighted learning framework. The proposed framework learns data-importance weights (either domain-level or instance-level) to optimize generalization performance on a held-out meta set aligned with target benchmarks. To the best of our knowledge, this is the first application of data reweighting to LLM-as-a-Judge training for test-time scaling. Our approach automatically emphasizes hard problems, in-distribution samples, and trajectory-aligned data, without relying on hand-crafted heuristics. Empirically, DAJ achieves state-of-the-art performance on LiveCodeBench and BigCodeBench, outperforming strong test-time scaling baselines as well as leading proprietary models.

Related papers

• JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation [13.831735556002426]
  Small language models (SLMs) have shown promise on various reasoning tasks.<n>Their ability to judge the correctness of answers remains unclear compared to large language models (LLMs)
  arXiv  Detail & Related papers  (2025-11-20T01:14:39Z)
• BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining [28.32850393150554]
  BLISS is a lightweight data selection method that operates entirely emphfrom scratch, without relying on any external pretrained oracle models.<n>We validate BLISS by pretraining 410M/1B/2.8B Pythia and LLaMA-0.5B models on selected subsets of the C4 dataset.<n> BLISS achieves $1.7times$ speedup in reaching the same performance as the state-of-the-art method.
  arXiv  Detail & Related papers  (2025-10-07T15:42:33Z)
• CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks [63.562924932512765]
  Large Language Models (LLMs) have advanced the state-of-the-art in various coding tasks.<n>LLMs can also serve as judges, assessing and comparing the quality of responses generated by other models.
  arXiv  Detail & Related papers  (2025-07-14T17:56:29Z)
• S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning [51.84977135926156]
  We introduce S$2$R, an efficient framework that enhances LLM reasoning by teaching models to self-verify and self-correct during inference.<n>Our results demonstrate that Qwen2.5-math-7B achieves an accuracy improvement from 51.0% to 81.6%, outperforming models trained on an equivalent amount of long-CoT distilled data.
  arXiv  Detail & Related papers  (2025-02-18T13:40:22Z)
• Training on the Benchmark Is Not All You Need [52.01920740114261]
  We propose a simple and effective data leakage detection method based on the contents of multiple-choice options.<n>Our method is able to work under gray-box conditions without access to model training data or weights.<n>We evaluate the degree of data leakage of 35 mainstream open-source LLMs on four benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T11:09:44Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• Towards Accelerated Model Training via Bayesian Data Selection [45.62338106716745]
  We propose a more reasonable data selection principle by examining the data's impact on the model's generalization loss. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss. This work solves these problems by leveraging a lightweight Bayesian treatment and incorporating off-the-shelf zero-shot predictors built on large-scale pre-trained models.
  arXiv  Detail & Related papers  (2023-08-21T07:58:15Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.