practitioner-guide-multi-turn-agentic-rl - Multi-turn Agentic RL Training Guide
Train LLM agents via multi-turn RL by optimizing environment complexity, reward signals, and policy initialization.
Tags
Updated: 2026-03-25Capabilities
Typical Inputs
Typical Outputs
What this skill does
- configure environment complexity
- design training curriculum
- implement dense rewards
- execute verified rewards
- initialize policy with SFT
- run PPO algorithm
- run GRPO algorithm
- compute GAE advantages
- evaluate on benchmarks
Inputs
- environment configuration
- reward signal definitions
- SFT demonstration data
- compute budget
- unit tests
- TextWorld environments
- ALFWorld environments
- SWE-Gym environments
Outputs
- trained agent policy
- benchmark evaluation results
- learning curves
- agent convergence status
Requirements
- Python framework veRL
- 7B+ LLM for complex tasks
- PPO or GRPO algorithm support
- 1000+ compute units budget