Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning
Quick Answer
The TRUST framework enhances decision-making in LLM-based agents by integrating uncertainty quantification into reward design, improving tool-use outcomes across benchmarks.
Quick Take
Experimental results indicate a consistent increase in decision quality and agent performance while providing reliable uncertainty estimates during optimization.
Key Points
- TRUST incorporates uncertainty quantification to improve decision-making in agents.
- Existing methods often lead to overconfident mistakes in decisions.
- Experimental results show enhanced decision quality across diverse benchmarks.
- The framework maintains reliable uncertainty estimates during optimization.
- Lightweight key-turn annotations are used for unified post-training.
Paper Resources
Source Excerpt
(LLM)-based agents often make suboptimal 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 and structured checklists, leaving the uncertainty characteristics of agent decisions underexplored. We observe that decision-oriented r
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