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A Taxonomy of RL Environments for LLM Agents

leehanchung.github.io · 3,049 words · saved by 1 readers

A structured guide to RL environments for LLM agents. RL environments are the training grounds that shape what agents can learn. This guide covers the five c...

Model architecture gets all the attention. Post-training recipes follow close behind. The reinforcement learning (RL) 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 single-turn Q&A will struggle the moment you ask it to maintain state across a 50-step enterprise workflow. A model trained with a poorly designed reward function will learn to game the metric and not solve the problem. Reinforcement learning environm

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