Multi-turn RL improves LLM agent long-horizon performance
Original title: What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?
Agentic RL gains concentrate in error recovery and long-horizon tasks but require sufficient interaction budgets.
Multi-turn reinforcement learning excels on long-horizon tasks yet remains sensitive to interaction constraints. Evaluation via Pass@(K,T) shows trajectory-level advantages diminish when budgets tighten. Agentic RL does not uniformly raise single-step quality; strength lies in error recovery. BudgetMix RL varies budgets during training for robust performance across settings.
- RL advantages depend on turn budgets
- Error recovery drives most gains
- BudgetMix RL varies interaction budgets
- Performance robust across execution settings
From the original article
1 min read at arxiv.orgWhile multi-turn agentic reinforcement learning (RL) excels on long-horizon tasks, its performance gains remain highly sensitive to interaction constraints. Evaluating across sampling breadth $K$ and interaction depth $T$ via $Pass@(K,T)$, we show that trajectory-level RL advantages depend on sufficient turn budgets, diminishing significantly when budgets tighten.
By Kejian Zhu · arxiv.org
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