Principle:Alibaba ROLL Agentic RL Configuration
| Knowledge Sources | |
|---|---|
| Domains | Reinforcement_Learning, Configuration, Agentic_AI |
| Last Updated | 2026-02-07 20:00 GMT |
Overview
A configuration management principle for defining environment-based reinforcement learning training of LLM agents with trajectory and step-level optimization parameters.
Description
Agentic RL Configuration extends standard PPO configuration with parameters specific to multi-turn, environment-interactive RL training. Unlike RLVR which operates on single-turn prompt-response pairs, the agentic configuration must specify:
- Environment manager settings: Which environments to use (Sokoban, FrozenLake, WebShop), trajectory vs step-level collection, group sizes for variance reduction
- Multi-level reward weighting: Episode-level vs step-level reward balance for algorithms like GiGPO
- Ratio computation type: Token-level (standard PPO) vs segment-level (GSPO) policy ratio computation
- Rollout parameters: Batch adjustment modes, partial GPU sharing between generation and training
The configuration validates that rollout batch sizes are divisible by group sizes, that generating arguments are consistent across inference clusters and environment managers, and that environment-specific settings are properly propagated.
Usage
Use this principle when setting up an agentic RL training pipeline that trains LLMs to interact with environments over multiple turns. Supports environments like Sokoban, FrozenLake, WebShop, and GEM.
Theoretical Basis
Agentic RL configuration brings together:
- Multi-turn MDP: The environment defines states, actions, and transitions across multiple dialogue turns
- GiGPO reward decomposition: Separating episode-level (global outcome) from step-level (intermediate progress) rewards with configurable weights
- Segment-level policy ratios: Computing importance ratios over entire response segments rather than individual tokens (GSPO)
Pseudo-code:
# Abstract agentic config structure
config.env_managers = [sokoban_config, frozenlake_config]
config.episode_reward_weight = 0.5
config.step_reward_weight = 0.5
config.ratio_type = "segment" # GSPO-style
config.adv_estimator = "gigpo"
Related Pages
Implemented By
Related Heuristics
The following heuristics inform this principle: