数据难度扩展：通过在新颖挑战性问题上的强化学习提升编程模型性能

Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems

March 8, 2026

作者: Zongqian Li, Tengchao Lv, Shaohan Huang, Yixuan Su, Qinzheng Sun, Qiufeng Yin, Ying Xin, Scarlett Li, Lei Cui, Nigel Collier, Furu Wei

cs.AI

摘要

训练下一代代码生成模型需要高质量数据集，但现有数据集面临难度失衡、格式不一致和数据质量问题。我们通过系统性数据处理与难度分级应对这些挑战，提出包含收集、处理、过滤和验证的四阶段数据处理框架，并引入基于大语言模型的自动难度过滤机制——该预测-校准-选择框架利用五维加权难度指标，在剔除简单问题的同时保留具有挑战性的题目。最终构建的MicroCoder数据集包含数万个来自多元平台的精选实时编程竞赛题目，突出时效性与高难度特性。在严格未见过的LiveCodeBench上的评估表明，相较于同等规模的常用基线数据集，MicroCoder在300个训练步数内实现3倍性能提升，且在GRPO及其变体训练算法下均保持稳定优势。该数据集在不同模型规模下对中高难度问题均带来显著改进，在模型能力极限处实现最高17.2%的相对性能增益。这些结果验证了难度感知的数据筛选能提升模型应对挑战性任务的能力，为代码生成领域的数据集构建提供了多重启示。

English

Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through systematic data processing and difficulty scaling. We introduce a four-stage Data Processing Framework encompassing collection, processing, filtering, and verification, incorporating Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework that leverages multi-dimensional difficulty metrics across five weighted dimensions to retain challenging problems while removing simplistic ones. The resulting MicroCoder dataset comprises tens of thousands of curated real competitive programming problems from diverse platforms, emphasizing recency and difficulty. Evaluations on strictly unseen LiveCodeBench demonstrate that MicroCoder achieves 3x larger performance gains within 300 training steps compared to widely-used baseline datasets of comparable size, with consistent advantages under both GRPO and its variant training algorithms. The MicroCoder dataset delivers obvious improvements on medium and hard problems across different model sizes, achieving up to 17.2% relative gains in overall performance where model capabilities are most stretched. These results validate that difficulty-aware data curation improves model performance on challenging tasks, providing multiple insights for dataset creation in code generation.