TRL Adds Agent Training Support Through OpenEnv
TRL library gains out-of-the-box support for training agents via OpenEnv integration with custom harnesses.
Retweets from Hugging Face engineers highlight that the TRL reinforcement learning library now integrates OpenEnv to enable training on agent harnesses without additional setup. Users can select any harness of their choice, including examples like open code, pi, codex, or claude code. This update streamlines agent training workflows in the TRL ecosystem by providing native support for flexible harness configurations. The change was announced through community posts on X, confirming the feature is available immediately for developers working with LLM post-training and related tools.
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TRL Adds Agent Training Support Through OpenEnv
TRL library gains out-of-the-box support for training agents via OpenEnv integration with custom harnesses.
Retweets from Hugging Face engineers highlight that the TRL reinforcement learning library now integrates OpenEnv to enable training on agent harnesses without additional setup. Users can select any harness of their choice, including examples like open code, pi, codex, or claude code. This update streamlines agent training workflows in the TRL ecosystem by providing native support for flexible harness configurations. The change was announced through community posts on X, confirming the feature is available immediately for developers working with LLM post-training and related tools.
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🚀🚀 TRL now supports training on agent harness out of the box through our OpenEnv integration. You can now train using harnesses like opencode 🧵
You can now train agents in TRL + OpenEnv with a harness of your choice. i.e. open code, pi, codex, claude code. This means you can improve the model in the context it is used; it's agent harness. It will learn to use the correct tools for the task, just as codex or claude code are trained. This is the process - take a datasets of tasks. - take a harness like open code which will drive the session. - take an openenv environment and harness adapter - use TRL HarnessRolloutWorker to collect the agent's traces - use AsyncGRPOTrainer to orchestrate inference and updating weights verifier returns env_reward