Open-SWE-Traces: Advancing Dual-Mode Multilingual Distillation for Software Engineering Agents

• URL: http://arxiv.org/abs/2606.16038v1
• Date: Sun, 14 Jun 2026 22:10:06 GMT
• Title: Open-SWE-Traces: Advancing Dual-Mode Multilingual Distillation for Software Engineering Agents
• Abstract summary: ourdataset is a dataset of 207,489 agentic trajectories spanning nine programming languages.<n>This data facilitates the training of models capable of long-horizon reasoning.<n>The best performing model achieves resolve rates of 61.7% on SWE-bench Verified, 57.1% on SWE-bench Multilingual, and 36.8% on SWE-bench Pro.
• Score: 42.7127695634038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The path toward autonomous software engineering is currently bottlenecked by a severe deficit of diverse, large-scale trajectory data. We address this by introducing \ourdataset, an expansive dataset of 207,489 agentic trajectories spanning nine programming languages (Python, Go, TS, JS, Rust, Java, PHP, C, C++). Sourced from 20,000 real-world PRs via OpenHands and SWE-agent harnesses, the dataset utilizes a hybrid-reasoning synthesis: Minimax-M2.5 generates trajectories with explicit "thinking" processes, while Qwen3.5-122B provides high-quality "non-thinking" traces. Filtered for permissive licenses (MIT, Apache, BSD) from SWE-rebench-V2, this data facilitates the training of models capable of long-horizon reasoning. We validate the dataset by fine-tuning the Qwen3-30B-A3B series (Thinking, Instruct, and Coder). The best performing model achieves resolve rates of 61.7% on SWE-bench Verified, 57.1% on SWE-bench Multilingual, and 36.8% on SWE-bench Pro. These results establish Open-SWE-Traces as a premier resource for distilling human-level software engineering capabilities into efficient, open-source agentic LLMs.

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