SWE Lego: Pushing the Limits of Supervised Fine tuning for Software Issue Resolving 🤗 HF Dataset • 🤗 SWE Lego Qwen3 8B/32B • 🧑💻 Code • 📖 Paper We present SWE Lego , a supervised fine tuning (SFT) recipe designed to achieve state of the art performance in software engineering (SWE) issue resolving. SWE Lego comprises three core building blocks: the SWE Lego dataset , a collection of 32k highquality task instances and 18k validated trajectories, combining real and synthetic data to complement each other in both quality and quantity; a refined SFT procedure with error masking and a difficulty based curriculum, which demonstrably improves action quality and overall performance; a well trained verifier for improving test time scaling (TTS). Our fine tuned models are trained exclusively with SFT from Qwen3 8B and Qwen3 32B. Their effectiveness is demonstrated on SWE Bench Verified: SWE Lego Qwen3 8B : 42.2% Pass@1, 49.6% TTS@16 SWE Lego Qwen3 32B : 52.6% Pass@1, 58.8% TTS@16 We’ve open sourced everything—our dataset, code, and training scripts, for everyone to progress on scaling and improving software engineering agents. Reproduction Guide 🎯 1. 📦 Installation 1.1 Installing vllm…
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