arXiv:2606.03762v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating external tools often destabilizes training: over-reliance on tools can induce input distribution shift, while overly conservative tool use limits effective exploration. To address this issue, we propose a unified framework TAO-RL that couples tool-aware trajectory filtering with entropy-guided exploration for efficient policy optimization. Specifically, at the data level, TA
The rapid advancement and integration of large language models necessitated solutions to stabilize their 'agentic' capabilities, particularly tool utilization, which is a current frontier in AI research.
Improving the efficiency and stability of agentic reinforcement learning is critical for developing more reliable and capable AI agents, which can automate complex tasks across industries.
The proposed TAO-RL framework offers a method to overcome instability and inefficiency in AI agents' tool use, potentially accelerating the development and deployment of robust agentic systems.
- · AI research labs
- · Enterprises adopting AI agents
- · Software developers building AI applications
- · Tasks requiring human oversight of unstable AI agents
- · Legacy automation solutions
More stable and efficient AI agents capable of complex tool use will emerge from research.
This stability will allow for wider deployment of AI agents in critical white-collar workflows, increasing automation.
The enhanced capabilities of AI agents could lead to significant re-evaluation of task allocation between humans and AI within organizations, compressing administrative layers.
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Read at arXiv cs.LG