Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement
arXiv:2605.26952v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has proven effective for training LLM-based agents with external tool-use capabilities. However, we identify that agentic RL training induces increasing redundant tool calls and blurs the model's intrinsic knowledge boundary, where the model fails to distinguish when tools are needed versus when parametric knowledge suffices. Existing solutions based on reward shaping create coarse-grained optimization targets that tend to incentivize indiscriminate tool-call suppression, leading to reward hacking. In this pape
The rapid advancement and deployment of LLM-based agents necessitate ongoing research into their operational efficiency and reliability, making investigations into their intrinsic knowledge and tool-use boundaries timely.
Improving the efficiency and reliability of AI agents by addressing issues of redundant tool calls and knowledge boundary confusion is crucial for their effective deployment in real-world, complex tasks and to prevent misallocation of computational resources.
The proposed method offers a more nuanced approach to optimizing agentic RL, moving beyond cruder reward shaping to enhance an agent's ability to discern when to use internal knowledge versus external tools.
- · AI developers
- · Companies deploying AI agents
- · Researchers in reinforcement learning
- · Inefficient AI agent models
- · Systems with high processing costs
AI agents will become more efficient and less prone to unnecessary external calls, reducing operational costs.
This efficiency gain could accelerate the adoption of complex agentic systems in various industries, broadening their application scope.
More reliable and discerning AI agents might lead to a re-evaluation of human-AI collaboration paradigms, shifting roles and responsibilities.
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