RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
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TL;DR - RTPO is a reinforcement-learning method that trains multi-turn, tool-using LLM agents by updating turns in reverse temporal order. It aims to stabilize agentic RL by improving credit assignment and preventing context mismatch and policy drift across trajectories.
- Represents multi-turn rollouts as sparse reverse trees and aligns each decision with its downstream continuation.
- Unifies three instability sources: rollout-training context mismatch, sparse-reward turn-level credit assignment, and asynchronous policy drift.
- Theoretical results claim elimination of context mismatch and asynchronous drift, reduced credit bias, and convergence to recursive optimality.
- On multi-turn agentic RL benchmarks, RTPO improves over trajectory-level and turn-level baselines by 21.50% and 10.76%, respectively.
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RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
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Hugging Face upvotes 2
TL;DR - RTPO is a reinforcement-learning method that trains multi-turn, tool-using LLM agents by updating turns in reverse temporal order. It aims to stabilize agentic RL by improving credit assignment and preventing context mismatch and policy drift across trajectories.
- Represents multi-turn rollouts as sparse reverse trees and aligns each decision with its downstream continuation.
- Unifies three instability sources: rollout-training context mismatch, sparse-reward turn-level credit assignment, and asynchronous policy drift.
- Theoretical results claim elimination of context mismatch and asynchronous drift, reduced credit bias, and convergence to recursive optimality.
- On multi-turn agentic RL benchmarks, RTPO improves over trajectory-level and turn-level baselines by 21.50% and 10.76%, respectively.