Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning

In three linesTRUST, a reinforcement learning method, improves LLM-based agents' tool-calling decisions by incorporating uncertainty quantification into reward design. Tested across multiple tool-use benchmarks, it reduces unsupported tool invocations and hallucinations while maintaining reliable uncertainty estimates.

Summary generated by Claude — human-verified