Year: 2026
Type: conference-paper
Abstract: High-stakes domains such as finance, law, and biomedicine demand both accurate results and rigorous reasoning.Current reinforcement learning paradigms primarily rely on outcomebased rewards, often overlooking latent logical fallacies in intermediate steps.Leveraging the cognitive asymmetry where falsifying local errors is more efficient than generating global correctness, we propose RADO (Reasoning Audit-Driven Optimization).RADO introduces a specialized audit model augmented with external tools to identify local logical ruptures and calibrate reward signals.By integrating Direct Preference Optimization (DPO) with Group Relative Policy Optimization (GRPO), our framework enables explicit supervision over reasoning paths.Experimental results demonstrate that RADO consistently improves final accuracy while significantly enhancing logical rigor in high-stakes domains.
Authors: Zhijie Tan, Xu Chu, Guanyu Wang, Ziyu Li, Weiping Li, Tong Mo
Institution: Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166810097