deepgym
RL environments with verifiable rewards you can trust. Sandboxed coding and computer-use tasks, verifiers kept apart from the answer, a QA gate against reward hacking, and infra failures kept out of training. Works with verl, TRL, OpenRLHF and Axolotl.
DeepGym runs a model's answer (code, a patch, shell commands or desktop actions) in a sandbox and scores it with a verifier kept apart from that answer. When the infrastructure fails instead of judging the answer, the run gets no verdict and can be kept out of training instead of counting as a 0. It ships agent harnesses for Claude Code, Codex and computer use, and plugs into verl, TRL, OpenRLHF, Axolotl or your own GRPO loop.
⚡ How to use deepgym
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/deepgym-deepgymCapabilities
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