Training Agents 4: From reward functions to environments.

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Training Agents, Session 4: from reward functions to environments. In this live session, the reward stops being a function and becomes a place the agent acts in. In Session 3 the model answered once and a Python function scored it. Today the model runs a loop: open a file, run a command, read the error, try again. The training signal comes from what happened after acting. We'll walk the reset()/step() contract from Gym to LLM agents, build an environment with OpenEnv, push it to the Hub, and plug it into TRL's GRPOTrainer with environment_factory. Then we go one step further and train a re