[2603.24477] Composer 2 Technical Report

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Computer Science > Software Engineering

arXiv:2603.24477 (cs)

[Submitted on 25 Mar 2026 (v1), last revised 26 Mar 2026 (this version, v2)]

Title:Composer 2 Technical Report

Authors:Cursor Research: Aaron Chan, Ahmed Shalaby, Alexander Wettig, Aman Sanger, Andrew Zhai, Anurag Ajay, Ashvin Nair, Charlie Snell, Chen Lu, Chen Shen, Emily Jia, Federico Cassano, Hanpeng Liu, Haoyu Chen, Henry Wildermuth, Jacob Jackson, Janet Li, Jediah Katz, Jiajun Yao, Joey Hejna, Josh Warner, Julius Vering, Kevin Frans, Lee Danilek, Less Wright, Lujing Cen, Luke Melas-Kyriazi, Michael Truell, Michiel de Jong, Naman Jain, Nate Schmidt, Nathan Wang, Niklas Muennighoff, Oleg Rybkin, Paul Loh, Phillip Kravtsov, Rishabh Yadav, Sahil Shah, Sam Kottler, Alexander M Rush, Shengtong Zhang, Shomil Jain, Sriram Sankar, Stefan Heule, Stuart H. Sul, Sualeh Asif, Victor Rong, Wanqi Zhu, William Lin, Yuchen Wu, Yuri Volkov, Yury Zemlyanskiy, Zack Holbrook, Zhiyuan Zhang<br>View a PDF of the paper titled Composer 2 Technical Report, by Cursor Research: Aaron Chan and 53 other authors

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Abstract: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.

Subjects:

Software Engineering (cs.SE); Machine Learning (cs.LG)

Cite as:<br>arXiv:2603.24477 [cs.SE]

(or<br>arXiv:2603.24477v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2603.24477

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arXiv-issued DOI via DataCite

Submission history<br>From: Alexander M. Rush [view email]<br>[v1]<br>Wed, 25 Mar 2026 16:18:37 UTC (1,616 KB)

[v2]<br>Thu, 26 Mar 2026 01:57:05 UTC (1,605 KB)

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