构建250万组代码问答与评注数据，提升大模型编程推理能力。
OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
- 通过两阶段微调，联合优化代码生成与评注能力。
- 在竞赛编程任务中表现优于或等于现有开源模型。
- 扩展LiveCodeBench支持C++，推动更全面的评估。
近期基于推理的大型语言模型（LLMs）在测试时缩放方面的进展，为代码生成与评注的蒸馏带来了新机遇。然而，这一领域的进步仍依赖于大规模高质量数据集。本文提出OpenCodeReasoning-II，包含250万组问题-解法-评注三元组（约3.5万个独立编程问题），规模接近此前最大公开代码推理数据集的两倍。采用两阶段监督微调策略：第一阶段专注代码生成微调，第二阶段联合训练生成与评注模型。所微调的Qwen2.5-Instruct模型在代码生成性能上达到或超越最佳现有开源蒸馏模型。尤为关键的是，生成与评注模型的融合显著提升了竞赛编程表现。此外，本文还将LiveCodeBench基准扩展至支持C++语言，进一步促进该基准在更广泛场景下的评估应用。
原文摘要 · Abstract (English)
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of the previous largest publicly available code reasoning dataset. In this work, we employ a two-stage supervised fine-tuning strategy. The first stage focuses on fine-tuning for code generation, while the second stage involves the joint training of models for both code generation and critique. Our resulting finetuned Qwen2.5-Instruct models achieve performance in code generation that either exceeds or equals the best prior open-weight distilled models. Notably, the integration of our code generation and critique models leads to significant improvements in competitive coding performance. Furthermore, we present an extension of the LiveCodeBench benchmark to specifically support the C++ programming language, thereby facilitating more comprehensive LLM evaluation using this benchmark.
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