LLMS

There are many good training methods for improving agents on SWE-bench: SWE-Gym, SWE-Smith, R2E-Gym. But what about broader software engineering tasks? In SWE-Playground, we introduce a new, more diverse synthetic data generation strategy to train divers software agents. Yiqi Zhu (@StephenZhu0218) Introducing SWE-Playground: A fully automated pipeline that generates synthetic environments to train versatile coding agents. 🤖✨ Training software engineering agents often relies on existing resources like GitHub issues and focuses on solving SWE-bench style issue resolution tasks. While this has driven incredible progress, real-world engineering involves a wider spectrum of tasks —from designing new libraries to writing reproduction scripts. 🌐 Rather than mining existing repositories, SWE-Playground synthetically generates projects, tasks, and verifiable unit tests from scratch. This approach offers two exciting opportunities: 1️⃣ Flexibility: We can generate tasks without being constrained by the availability or structure of existing open-source data. 2️⃣ Versatility: We extend training beyond Issue Resolution to include Issue Reproduction and Library Generation from Scratch. The results? 🚀 Our agents achieve strong performance across SWE-bench Verified, SWT-Bench, and Commit-0, demonstrating high data efficiency compared to baselines trained on larger datasets. Huge thanks to my amazing collaborators @apurvasgandhi and @gneubig for their incredible efforts on bringing this work to life! 👇 🧵 A deep dive into how we build versatile agents synthetically. Paper: arxiv.org/pdf/2512.12216 Project Page: neulab.github.io/SWE-Playgro… Code: github.com/neulab/SWE-Playgr… Data & Models: huggingface.co/collections/S… — https://nitter.net/StephenZhu0218/status/2000754124019683469#m

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