Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching

2025-07-05

AI Summary

Existing preference optimization methods—such as DPO and GRPO—perform well in single-turn tasks but fail to capture the dynamic influence of early decisions on subsequent diagnostic trajectories in multi-turn medical dialogues. Method: We propose a branching reinforcement learning framework grounded in a “dialogue forest” structure, which explicitly models multi-path dialogue evolution via a tree-based representation. This approach links early-turn decision biases to final diagnostic outcomes, generating rich cross-turn training signals. By unifying DPO and GRPO within a generative RL paradigm, our method jointly optimizes policies over the multi-path dialogue tree. Contribution/Results: Evaluated on simulated doctor–patient diagnostic dialogues, our framework significantly improves multi-turn diagnostic accuracy. Empirical results validate the efficacy of the branching structure in modeling clinical decision dynamics, establishing a novel paradigm for fine-tuning conversational LLMs in multi-turn medical settings.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Fine-tuning methods such as Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) have demonstrated success in training large language models (LLMs) for single-turn tasks. However, these methods fall short in multi-turn applications, such as diagnostic patient interviewing, where understanding how early conversational turns influence downstream completions and outcomes is essential. In medicine, a multi-turn perspective is critical for learning diagnostic schemas and better understanding conversation dynamics. To address this gap, I introduce Savage Conversation Forests (SCF), a reinforcement learning framework that leverages a branched conversation architecture to fine-tune LLMs for multi-turn dialogue. SCF generates multiple possible conversation continuations at each turn, enabling the model to learn how different early responses affect downstream interactions and diagnostic outcomes. In experiments simulating doctor-patient conversations, SCF with branching outperforms linear conversation architectures on diagnostic accuracy. I hypothesize that SCF's improvements stem from its ability to provide richer, interdependent training signals across conversation turns. These results suggest that a branched training architecture is an important strategy for fine tuning LLMs in complex multi-turn conversational tasks.

Problem

• Enhancing LLMs for multi-turn medical dialogue accuracy
• Addressing gaps in early turn influence on outcomes
• Improving diagnostic schemas via branched conversation training

Innovation

• Branched conversation architecture for multi-turn dialogue
• Generates multiple conversation continuations per turn
• Reinforcement learning framework with interdependent training signals