Principle:Alibaba ROLL Environment Management
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Agentic_AI, Environment_Interaction |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
An environment orchestration principle for managing multi-turn LLM-environment interactions with trajectory collection, group-based variance reduction, and concurrent episode execution.
Description
Environment Management handles the interaction loop between an LLM agent and an external environment (Sokoban, FrozenLake, WebShop, etc.). The principle covers two modes of operation:
- Trajectory-level (TrajEnvManager): Collects complete episodes as single training samples. The LLM generates responses turn-by-turn, interacting with the environment until termination. Suitable for standard RL algorithms like PPO and GRPO.
- Step-level (StepEnvManager): Creates one training sample per interaction step, with history windowing. Each step becomes an independent training example. Suitable for step-wise algorithms like GiGPO.
Both modes support group-based variance reduction: multiple episodes are collected with the same initial state (seed) to enable relative advantage computation within groups.
Usage
Use this principle when training LLM agents to interact with environments over multiple turns. The choice between trajectory-level and step-level collection depends on the advantage estimation algorithm.
Theoretical Basis
The environment interaction follows a standard MDP loop:
Pseudo-code:
# Abstract trajectory collection
for group_id in range(num_groups):
for episode in range(group_size):
state = env.reset(seed=group_seed + episode)
trajectory = []
while not done:
action = llm.generate(format_history(trajectory, state))
next_state, reward, done = env.step(action)
trajectory.append((state, action, reward))
state = next_state
output_queue.put(formulate_training_sample(trajectory))
Related Pages
Implemented By
Related Heuristics
No specific heuristics inform this principle.