Multi-turn agentic reinforcement learning (RL) improves long-horizon task performance, but those gains are highly sensitive to interaction constraints, according to an evaluation using Pass@(K,T) across sampling breadth K and interaction depth T. The evaluation shows trajectory-level RL advantages depend on sufficient interaction depth, indicating that reported gains for LLM agents hinge on how many turns and samples the agent is allowed.
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 t
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