PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue

arXiv:2609.27987 · cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• GRPO — 51× in this paper · explained in 6 episodes
• Benchmark — 40× in this paper · explained in 2 episodes
• Accuracy — 37× in this paper · explained in 1 episode
• Actor — 20× in this paper · explained in 3 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Privileged Counterfactual Question Credit — 5× in this paper
• Information Gain — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Credit assignment — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes

Sources

• ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
• InfoPO: Information-Driven Policy Optimization for User-Centric Agents
• Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• TIPS: Turn-Level Information-Potential Reward Shaping for Search-Augmented LLMs

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