Training Coding Agents with RL: OpenCode Harness in HF Sandboxes
SergioPaniego · x · 2026-08-05
A new blog post details how to train a coding agent using Reinforcement Learning (RL) in a real coding environment.
Core Workflow:
- Isolated Execution: Uses OpenCode as the agent harness, running inside OpenEnv remote sandboxes. Each rollout is isolated, allowing scaling beyond a single machine.
- Token Recording: An in-sandbox proxy records the real token IDs and logprobs for every turn of the agent's tool loop.
- Reward Mechanism: A hidden-test verifier scores the execution results, which serves as the reward signal.
- Model Training: Utilizes the TRL library with AsyncGRPO for training, syncing weights back to vLLM over NCCL.
The blog includes a runnable example for developers to test and build upon.
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