While 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 in
While 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. Besides, agentic RL does not uniformly elevate single-step decision quality; rather, its strength concentrates in error recovery and long-horizon resilience. To mitigate the deployment risks caused by this budget dependence, we provide a preliminary discussion on the benefits and limitations of process supervision signals and introduce…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents? was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 19 checks on 2 different days. Its best position was #4 of 30, reached on Nov 6, 03:28 UTC. It dropped off the list after Nov 6, 19:28 UTC.
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It was first seen on Nov 5, 2026 and has been on the list on 2 days (19 checks) since.