aiqbench analysis
The proposed TRUST framework addresses critical reliability issues in LLM agents by integrating uncertainty quantification directly into reinforcement learning reward design to prevent overconfident tool-use errors. By maintaining distinct uncertainty levels between correct and incorrect actions, this approach improves decision quality and exploration signals without relying solely on coarse-grained outcome corrections. This method offers a robust pathway for enhancing the stability and accuracy of multi-step agentic workflows in complex environments.
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 and structured checklists, leaving the uncertainty characteristics of agent decisions underexplored. We observe that decision-oriented reinforcement learning tends to weaken the uncertainty separation between correct and incorrect actions, resulting in overconfident mistakes and weaker exploration signals. Therefore, we propose TRUST, which incorporates uncertainty quantification into reward design as a repulsive force for maintaining uncertainty separation, and labels lightweight key-turn annotations for unified post-training of multi-turn trajectories. Experimental results across diverse tool-use benchmarks show that TRUST consistently enhances both decision quality and agent performance while maintaining more reliable uncertainty estimates during optimization.
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 and structured checklists, leaving the uncertainty characteristics of agent decisions underexplored. We observe that decision-oriented reinforcement learning tends to weaken the uncertainty separation between correct and incorrect actions, resulting in overconfident mistakes and weaker exploration signals. Therefore, we propose TRUST, which incorporates uncertainty quantification into reward design as a repulsive force for maintaining uncertainty separation, and labels lightweight key-turn annotations for unified post-training of multi-turn trajectories. Experimental results across diverse tool-use benchmarks show that TRUST consistently enhances both decision quality and agent performance while maintaining more reliable uncertainty estimates during optimization.
Artificial Intelligence (cs.AI)
arXiv:2606.06976 [cs.AI]
(or arXiv:2606.06976v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06976 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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