Hi everyone, super excited to have wrapped up and released the CodeScout paper! For more context, this work proposes an effective RL recipe for training code agents to search relevant locations in a repository using only a terminal 🚀 • Prior methods have relied on complex inference scaffolds (for e.g. using code graphs) that are Python-specific and in many cases are very expensive for deployment/RL! • CodeScout uses standard Unix command-line utilities (like ripgrep, sed) to localize relevant files, classes and functions of code, and achieves SoTA results on SWE-Bench Verified, Pro, and Lite outperforming 8-18x larger base and post-trained LLMs, closing the gap with frontier closed-source models🏆 • Interestingly, our 14B model achieves strong results using just two command-line utilities: ripgrep and sed ✨ Here are the social media posts if you are interested in reading more/sharing our work: • https://x.com/Aditya_Soni_8/status/2035008866438561874 • https://www.linkedin.com/posts/lintang-sutawika_can-we-train-code-agents-to-search-re[…]m=member_desktop&rcm=ACoAADnDWxMBQ6OdTTt3JfRYQO98LesPPFYbrXs Paper: https://arxiv.org/abs/2603.17829 Code: https://github.com/OpenHands/codescout HF Collection: https://huggingface.co/collections/OpenHands/codescout Huge thanks to all our collaborators for making this project a success 🤩