arXiv:2603.24477cs.SEcs.LG2026-03被引 16

Composer 2 是专为软件工程设计的智能编码模型,能高效完成复杂长期任务。

Composer 2 Technical Report

  • 分两阶段训练:先持续预训练,再大规模强化学习提升推理与执行能力。
  • 在CursorBench上准确率达61.3,较前代显著提升;SWE-bench多语言得分73.7。
  • 基于真实代码库构建评测基准,适合研究领域专用模型训练方法者。

Composer 2 是一种专用于智能软件工程的模型,具备强大的长期规划与编程智能,同时保持交互式使用下的高效问题解决能力。模型采用两阶段训练:首先进行持续预训练以增强知识储备和潜在编码能力,随后通过大规模强化学习,提升端到端编码性能,包括更强的推理能力、多步执行的准确性以及长周期真实编码任务中的连贯性。我们构建了与部署模型所用Cursor环境一致的训练基础设施,配备相同工具与结构,并采用贴近真实问题的环境。为评估模型在日益复杂任务上的表现,我们引入了一个源自大型代码库(含自身项目)真实软件工程问题的基准。Composer 2 属于前沿级编码模型,展示了训练强领域专用模型的有效路径。在CursorBench评测中,其准确率相较以往版本显著提升至61.3;在公开基准上,终端任务得分为61.7,SWE-bench Multilingual得分为73.7,达到当前最先进系统水平。

原文摘要 · Abstract (English)

Composer 2 is a specialized model designed for agentic software engineering. The model demonstrates strong long-term planning and coding intelligence while maintaining the ability to efficiently solve problems for interactive use. The model is trained in two phases: first, continued pretraining to improve the model's knowledge and latent coding ability, followed by large-scale reinforcement learning to improve end-to-end coding performance through stronger reasoning, accurate multi-step execution, and coherence on long-horizon realistic coding problems. We develop infrastructure to support training in the same Cursor harness that is used by the deployed model, with equivalent tools and structure, and use environments that match real problems closely. To measure the ability of the model on increasingly difficult tasks, we introduce a benchmark derived from real software engineering problems in large codebases including our own. Composer 2 is a frontier-level coding model and demonstrates a process for training strong domain-specialized models. On our CursorBench evaluations the model achieves a major improvement in accuracy compared to previous Composer models (61.3). On public benchmarks the model scores 61.7 on Terminal-Bench and 73.7 on SWE-bench Multilingual in our harness, comparable to state-of-the-art systems.

代码生成强化学习智能工程领域模型

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