Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits
Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and tool-use in interactive environments. However, existing agent RL methods remain fundamentally constrained by an on-policy reasoning bottleneck: when tasks exceed the mode...
Xi Chen, Rui-Xue Ding, Wang Zhigaoyuan et al.
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