Principle:OpenRLHF OpenRLHF Agent Based Rollout Collection
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Agent_Execution, Inference |
| Last Updated | 2026-02-07 10:40 GMT |
Overview
Framework for collecting agent-environment interaction rollouts during reinforcement learning training of language models.
Description
Agent-based rollout collection extends standard RL training by introducing an environment loop where the language model (policy) interacts with external environments or evaluation systems. Instead of generating a single response per prompt, the agent may engage in multi-turn interactions, receiving environment feedback (observations, rewards) at each step. This enables training on tasks that require sequential decision-making, tool use, or game playing. The framework decouples the policy model (served via vLLM) from the environment interaction logic, allowing different environments (NeMo Gym, GEM games) to be plugged in via executor classes.
Usage
Use agent-based rollout collection when training language models on tasks that require environment interaction, such as mathematical problem solving with verification, game playing, or multi-turn tool use. The agent executor manages the interaction loop, collecting trajectories with step-level rewards for PPO or REINFORCE training.
Theoretical Basis
The agent-environment loop follows the standard RL formulation:
Where at each step:
- The agent observes state (text observation)
- The policy generates action (text response)
- The environment returns reward and next state
Pseudo-code Logic:
# Abstract agent-environment loop (NOT actual implementation)
observation = environment.reset(prompt)
trajectory = []
done = False
while not done:
action = policy.generate(observation)
reward, next_observation, done = environment.step(action)
trajectory.append((observation, action, reward))
observation = next_observation
# Use trajectory for PPO/REINFORCE training
advantages = compute_advantages(trajectory)
update_policy(policy, trajectory, advantages)