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ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
technology ai_agents March 5, 2026 1 source · confidence 5/10
#Reinforcement Learning
#Medical AI
#LLM Alignment
#ATPO
#Multi-turn 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.
5D Score
Capital Relevance
technological 9/10
informational 8/10
economic 7/10
physical 7/10
temporal 6/10
cultural 4/10
symbolic 3/10
social 2/10
psychological 1/10
Agent API
/api/v1/intel/6
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