TL;DR: A new pipeline automatically builds 5,545 RL training tasks for coding agents using only source code itself, no issues or commit history needed, and it prioritizes quality over quantity. Title: CodeMidas: Scaling Agentic Coding RL Environments from Code Itself URL: Key points 🏗️ Auto-builds 5,545 tasks from 3,185 repos across 23 languages and 15 domains 🧪 Generates verifiers via execution-grounded tests, running the reference solution to record expected outputs 🛡️ Three-stage filtering: leakage checks, agent-solution agreement, and rollout difficulty filtering 📈 Big gains after RL training: DeepSWE +11.7pt, ProgramBench +17.0pt, Terminal-Bench +8.5pt 🔍 5k filtered tasks consistently beat 8k unfiltered tasks, proving quality beats quantity 🧠 Trained agents explore more and self-verify more, and these behaviors transfer to external benchmarks I like how simple and practical the core idea is: you don't need issue trackers or dev history to build RL environments, just the code. #CodingAgent# #ReinforcementLearning#