What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?
Multi-turn RL’s gains concentrate in error recovery and sustained performance, and shrink when agents get fewer turns.
The authors evaluate agents across both the number of sampled attempts and the allowed interaction depth. They find that multi-turn reinforcement learning does not consistently improve individual decisions, while its trajectory-level advantages depend on sufficient turns. Their alternative, BudgetMix RL, varies interaction budgets during training and delivers robust performance across the execution settings tested.
HF Daily Papers' note
The authors evaluate agents across both the number of sampled attempts and the allowed interaction depth. They find that multi-turn reinforcement learning does not consistently improve individual decisions, while its trajectory-level advantages depend on sufficient turns. Their alternative, BudgetMix RL, varies interaction budgets during training and delivers robust performance across the execution settings tested.
HF Daily Papers' note
score 5