AIBullisharXiv – CS AI · 5h ago6/10
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Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning
Researchers propose TRUST, a reinforcement learning framework that improves LLM-based agent decision-making by incorporating uncertainty quantification into reward design. The approach addresses a critical flaw where standard RL weakens the distinction between correct and incorrect tool-use decisions, leading to overconfident mistakes and reduced exploration capabilities.