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The Training Grounds: A Taxonomy of RL Environments for LLM Agents

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The Training Grounds: A Taxonomy of RL Environments for LLM Agents Model architecture gets all the attention. Post-training recipes follow close behind. The training environment — what the model actually practices on, how its work gets judged, what tools it can use — barely enters the conversation. That’s the part that actually determines what the agent can learn to do. A model trained only on sin...
This analysis of RL environments for LLM agents offers a robust framework for understanding how training grounds shape agent capabilities. The strongest version of this narrative is its emphasis on the often-overlooked role of environment design in determining what an agent can learn. By breaking down environments into task distribution, harness, verifier, state, and configuration, it provides a clear taxonomy for practitioners to evaluate and improve their training setups. The discussion of arc...