Training Agents 4: From Reward Functions to Environments, Toward Real Coding Agents
ORIGINAL / Training Agents 4: From reward functions to environments.
This session demonstrates how RL training of agents moves from simple reward functions to environment interaction, with performance gains in coding tasks by integrating environments with TRL's GRPOTrainer.
01 ABSTRACT
The video introduces the fourth part of training agents, focusing on shifting from reward functions to environments. It demonstrates building environments with OpenEnv and training coding agents with TRL, showing performance improvements in MBPP and DeepCoder tasks.
02 KEY FINDINGS
- Reward functions are insufficient for multi-step agents; environments are needed.
- reset()/step() becomes the standard interface for agent-environment interaction.
- An RL environment includes task, tools, backend, reward, and episode.
- OpenEnv ecosystem connects Verifiers, OpenReward, and Harbor.
- TRL calls env via environment_factory, replacing reward_funcs.
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