mlsanityData to 7 Oct 2026

Composer 2 Technical Report

arXiv 2603.24477 ↗First posted 25 Mar 2026cs.SE cs.LGcited by 20

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 62 of its 83 references that are in the map. First posted on arXiv 25 Mar 2026.

Builds on

62 of 83 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026GLM-5: from Vibe Coding to Agentic Engineering17 Feb
  2. 2025SWE-smith: Scaling Data for Software Engineering Agents30 Apr
  3. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  4. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  5. 2023ChatDev: Communicative Agents for Software Development16 Jul

Other references · 57

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  4. 2025Qwen3 Technical Reportcited by 8,195
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  7. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  8. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  9. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  10. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
  11. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  12. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
38 more
  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  2. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  3. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  4. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  5. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,278
  6. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,276
  7. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,193
  8. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
  9. 2024Qwen2.5-Coder Technical Reportcited by 1,151
  10. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 948
  11. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  12. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 876
  13. 2023StarCoder: may the source be with you!cited by 790
  14. 2025Group Sequence Policy Optimizationcited by 788
  15. 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 755
  16. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  17. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 641
  18. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  19. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  20. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  21. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 484
  22. 2026Kimi K2.5: Visual Agentic Intelligencecited by 449
  23. 2023Ring Attention with Blockwise Transformers for Near-Infinite Contextcited by 448
  24. 2024Better & Faster Large Language Models via Multi-token Predictioncited by 344
  25. 2023CodeT5+: Open Code Large Language Models for Code Understanding and Generationcited by 327
  26. 2024DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligencecited by 325
  27. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMscited by 276
  28. 2023Magicoder: Empowering Code Generation with OSS-Instructcited by 251
  29. 2023DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Modelscited by 239
  30. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 178
  31. 2023SantaCoder: don't reach for the stars!cited by 169
  32. 2024CodeGemma: Open Code Models Based on Gemmacited by 145
  33. 2025Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Modelscited by 134
  34. 2024Granite Code Models: A Family of Open Foundation Models for Code Intelligencecited by 116
  35. 2020PyMT5: multi-mode translation of natural language and Python code with transformerscited by 80
  36. 2025AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergycited by 75
  37. 2025Pretraining Large Language Models with NVFP4cited by 67
  38. 2025Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routerscited by 67

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 20.

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  1. 2026SWE-chat: Coding Agent Interactions From Real Users in the Wildcited by 36
  2. 2026Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agentscited by 16
  3. 2026Agent-as-a-Router: Agentic Model Routing for Coding Taskscited by 10
  4. 2026CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Taskscited by 5
  5. 2026GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learningcited by 3
  6. 2026Poly-EPO: Training Exploratory Reasoning Modelscited by 3
  7. 2026Guardrails Beat Guidance: A Large-Scale Study of Rules, Skills, and Persistent Configuration for Coding Agentscited by 2
  8. 2026Prefix Sliding for efficient test-time scalingcited by 2
  9. 2026Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teamscited by 1
  10. 2026(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understandingcited by 1
  11. 2026Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Modelscited by 0
  12. 2026Beyond Distribution Sharpening: The Importance of Task Rewardscited by 0
8 more
  1. 2026Safe to Check, Unsafe to Use: Relinking at the Compression Boundary of LLM Agentscited by 0
  2. 2026Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositoriescited by 0
  3. 2026AgentOmnia: Scaling Agentic Models for Full-Scenario Applicationscited by 0
  4. 2026Towards a Risk Assessment of Malicious Skill Files in Coding Agentscited by 0
  5. 2026Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settingscited by 0
  6. 2026Long-Horizon Language Model Reinforcement Learning via Progressive Point Matchingcited by 0
  7. 2026Mixture-of-Kittens: MoE Megakernel for NVL72scited by 0
  8. 2026Context Language Modelscited by 0

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  2. 2025KAT-Coder Technical Report
  3. 2026RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades
  4. 2026FormulaCode: Evaluating Agentic Optimization on Large Codebases
  5. 2025SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
  6. 2026Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
  7. 2026daVinci-Dev: Agent-native Mid-training for Software Engineering
  8. 2026Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality