{"data":{"id":6,"backendId":"fed63174-f8e3-42ce-8782-f4645c91c42b","title":"ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue","summary":"arXiv:2603.02216v1 Announce Type: new Abstract: Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the uncertainty inherent in user-agent interactions, which we formulate as a Hierarchical Markov Decision Process (H-MDP). While conventional Reinforcement Learning (RL) methods like Group Relative Policy Opti","analysis":"This research addresses critical stability and credit assignment issues in standard RL (PPO/GRPO) for complex, multi-turn medical agent interactions.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{"social":2,"cultural":4,"economic":7,"symbolic":3,"technological":9,"informational":8,"temporal":6,"psychological":1,"physical":7},"tags":["Reinforcement Learning","Medical AI","LLM Alignment","ATPO","Multi-turn Dialogue"],"qualityScore":10,"valueScore":8,"interestScore":8,"potentialScore":9,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-05T00:09:43.334Z","createdAt":"2026-03-05 00:11:22"}}