Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv

TRL’s OpenEnv integration enables “loop-owning” reinforcement learning for coding agents, allowing an unmodified harness such as OpenCode to execute its normal tool-use loop in isolated sandboxes while TRL trains on the exact model tokens, log probabilities, and outcomes it produces. In the demonstrated setup, each rollout runs in a remote Hugging Face sandbox with a prebuilt OpenCode and proxy image, hidden DeepCoder tests evaluate the completed workspace, and AsyncGRPO applies rewards to action-producing turns. Training can be launched as a Hugging Face Jobs workflow that serves vLLM locally for trainer synchronization while exposing a separate reachable endpoint for remote sandboxes, though public tunnels should be replaced with access-controlled infrastructure for nonexperimental use. Remote execution can scale cheaply across many CPU sandboxes but introduces startup failures, cleanup requirements, and asynchronous lag. In a short experiment on 32 tasks, Qwen3-8B improved from roughly 0.27 to 0.71 reward over 10 steps, whereas a 4B model’s reward initially rose then collapsed into ineffective tool-call behavior. The integration is presented as an early, manually configured implementation, with future work intended to unify multiple coding-agent harnesses and sandbox backends through Harbor.

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