How can we boost LLM agents’ generalizability to OOD tasks and environments? Check out CodeGym, our new project for synthesizing environments for LLM agent RL training. CodeGym is a synthetic environment generation framework for reinforcement learning on multi-turn tool-use tasks. It automatically converts static coding problems into interactive and verifiable RL training environments. Training in CodeGym leads to strong OOD generalization — for example, a Qwen2.5-32B-Instruct model achieved an 8.7-point absolute accuracy gain on τ-Bench! We’ve just released the paper, synthesis pipeline, and dataset: 📄 Paper: arxiv.org/abs/2509.17325 💻 Project: github.com/StigLidu/CodeGym 📊 Dataset: huggingface.co/datasets/Vani… 📷 More details in the thread👇