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

Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning arXiv:2606.06976v1 Announce Type: new Abstract: Large language model (LLM)-based agents often make suboptimal tool-use decisions, including unsupported tool invocation and hallucinated direct responses, which may accumulate errors throughout multi-step interactions. Existing approaches mainly improve these behaviors through inference-time correction or coarse-grained reward signals based on decision outcomes