Data to 7 Oct 2026

SWE-smith: Scaling Data for Software Engineering Agents

arXiv 2504.21798 ↗First posted 30 Apr 2025cs.SE cs.AI cs.CLcited by 255

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

Abstract (arXiv)

Despite recent progress in Language Models (LMs) for software engineering, collecting training data remains a significant pain point. Existing datasets are small, with at most 1,000s of training instances from 11 or fewer GitHub repositories. The procedures to curate such datasets are often complex, necessitating hundreds of hours of human labor; companion execution environments also take up several terabytes of storage, severely limiting their scalability and usability. To address this pain point, we introduce SWE-smith, a novel pipeline for generating software engineering training data at scale. Given any Python codebase, SWE-smith constructs a corresponding execution environment, then automatically synthesizes 100s to 1,000s of task instances that break existing test(s) in the codebase. Using SWE-smith, we create a dataset of 50k instances sourced from 128 GitHub repositories, an order of magnitude larger than all previous works. We train SWE-agent-LM-32B, achieving 40.2% Pass@1 resolve rate on the SWE-bench Verified benchmark, state of the art among open source models. We open source SWE-smith (collection procedure, task instances, trajectories, models) to lower the barrier of entry for research in LM systems for automated software engineering. All assets available at https://swesmith.com.

Key facts. Cited by 255 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 48 of its 59 references that are in the map. First posted on arXiv 30 Apr 2025.

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48 of 59 references.

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Same direction, newest first

Other references · 39

2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
2021Evaluating Large Language Models Trained on Codecited by 8,053
2024Qwen2.5 Technical Reportcited by 4,713
2024GPT-4o System Cardcited by 4,467
2021Program Synthesis with Large Language Modelscited by 3,509
2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
2024Qwen2.5-Coder Technical Reportcited by 1,151
2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728

27 more

2023Cognitive Architectures for Language Agentscited by 422
2023Scaling Relationship on Learning Mathematical Reasoning with Large Language Modelscited by 417
2024AutoCodeRover: Autonomous Program Improvementcited by 272
2023Magicoder: Empowering Code Generation with OSS-Instructcited by 251
2023FireAct: Toward Language Agent Fine-tuningcited by 247
2025SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolutioncited by 246
2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 229
2024SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?cited by 196
2023OctoPack: Instruction Tuning Code Large Language Modelscited by 178
2025Multi-SWE-bench: A Multilingual Benchmark for Issue Resolvingcited by 165
2023Language Models Meet World Models: Embodied Experiences Enhance Language Modelscited by 123
2024AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorialscited by 123
2023Learning Performance-Improving Code Editscited by 103
2025SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolutioncited by 87
2022Language Models Can Teach Themselves to Program Bettercited by 75
2024SelfCodeAlign: Self-Alignment for Code Generationcited by 61
2024NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wildcited by 55
2024SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositoriescited by 52
2024Commit0: Library Generation from Scratchcited by 50
2024Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scalecited by 43
2023WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuningcited by 42
2024ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Datacited by 39
2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
2025Automated Benchmark Generation for Repository-Level Coding Taskscited by 27
2024Learning Code Preference via Synthetic Evolutioncited by 19
2025RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testingcited by 12
2024DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 65; 2026: 190.

255 papers.

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2025Kimi K2: Open Agentic Intelligencecited by 523
2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 502
2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Taskscited by 289
2025CWM: An Open-Weights LLM for Research on Code Generation with World Modelscited by 87
2025Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoningcited by 85
2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectoriescited by 75
2025SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Taskscited by 69
2025FutureX: An Advanced Live Benchmark for LLM Agents in Future Predictioncited by 55
2025SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolutioncited by 52
2025Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Modelscited by 45
2025SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agentcited by 44
2025The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Managementcited by 40

38 more

2025REAP the Experts: Why Pruning Prevails for One-Shot MoE compressioncited by 40
2026Code as Agent Harnesscited by 40
2025Context as a Tool: Context Management for Long-Horizon SWE-Agentscited by 38
2026SWE-chat: Coding Agent Interactions From Real Users in the Wildcited by 36
2025Kimi-Dev: Agentless Training as Skill Prior for SWE-Agentscited by 35
2025Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecyclecited by 34
2026On Data Engineering for Scaling LLM Terminal Capabilitiescited by 34
2025InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptationcited by 31
2026FeatureBench: Benchmarking Agentic Coding for Complex Feature Developmentcited by 30
2026LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfacescited by 29
2025ARE: Scaling Up Agent Environments and Evaluationscited by 28
2025BugPilot: Complex Bug Generation for Efficient Learning of SWE Skillscited by 28
2026Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environmentscited by 27
2025Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMscited by 24
2026TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agentscited by 24
2026SWE-rebench V2: Language-Agnostic SWE Task Collection at Scalecited by 24
2026Tmax: A simple recipe for terminal agentscited by 24
2025STARK: Strategic Team of Agents for Refining Kernelscited by 23
2025Cyber-Zero: Training Cybersecurity Agents without Runtimecited by 22
2026CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agentscited by 21
2025How to Train Your Advisor: Steering Black-Box LLMs with Advisor Modelscited by 20
2026Immersion in the GitHub Universe: Scaling Coding Agents to Masterycited by 20
2026CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversioncited by 20
2026BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?cited by 20
2026Composer 2 Technical Reportcited by 20
2026SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?cited by 19
2025InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Researchcited by 18
2025Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Taskscited by 18
2025Language Models Can Learn from Verbal Feedback Without Scalar Rewardscited by 17
2025Process-Level Trajectory Evaluation for Environment Configuration in Software Engineering Agentscited by 17
2025Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineeringcited by 16
2025Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agentscited by 16
2025Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMscited by 16
2026SWE-Universe: Scale Real-World Verifiable Environments to Millionscited by 16
2026Toward Scalable Terminal Task Synthesis via Skill Graphscited by 16
2025Training Language Model Agents to Find Vulnerabilities with CTF-Dojocited by 15
2025A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic Systemcited by 15
2025Tool-integrated Reinforcement Learning for Repo Deep Searchcited by 14

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