标题： OpenCodeReasoning-II：通过自我批判的简单测试时缩放方法 显示英文标题

标题： OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique

摘要： 基于推理的大型语言模型（LLMs）的最新进展，特别是其通过测试时缩放的潜力，为代码生成和批判的蒸馏创造了重大机会。 然而，这两个领域的进展根本上依赖于大规模、高质量的数据集。 在本工作中，我们引入了OpenCodeReasoning-II，一个包含250万个问题-解决方案-批判三元组的数据集（约35,000个独特的编程问题），使其大小几乎是之前最大公开可用代码推理数据集的两倍。 在本工作中，我们采用了一个两阶段的监督微调策略。 第一阶段专注于代码生成的微调，而第二阶段涉及代码生成和批判模型的联合训练。 我们得到的微调后的Qwen2.5-Instruct模型在代码生成方面的性能超过了或等于最佳先前开放权重蒸馏模型。 值得注意的是，我们的代码生成和批判模型的集成显著提高了竞争性编程性能。 此外，我们对LiveCodeBench基准进行了扩展，以专门支持C++编程语言，从而促进了使用此基准的更全面的LLM评估。 显示英文摘要

摘要： 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.