# HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

**Authors:** Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, Benyou Wang  
**arXiv:** [2412.18925](https://arxiv.org/abs/2412.18925)  
**Date:** 2024-12-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2412.18925v1/

## Key points

- Authors discuss the need to enhance reasoning in Language Model (LLM) systems for medical applications
- Previous research has focused on mathematical tasks, but domains like medicine require robust reasoning capabilities
- Medical decision-making processes are complex and can significantly impact patient outcomes
- Traditional LLMs have limitations in performing effective reasoning tasks
- The authors propose a two-stage process to improve medical reasoning
- They introduce HuatuoGPT-o1 model to address the issue and ensure correctness of outputs
- Experiments show that HuatuoGPT-o1 outperforms general-purpose and medical-specific baselines in solving verifiable problems
- Complex reasoning significantly improves medical problem-solving and benefits from reinforcement learning techniques
- The authors aim to inspire advancements in reasoning across various specialized domains beyond just medicine

## Summary

In their paper titled "HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs," authors Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, and Benyou Wang discuss the potential of enhancing reasoning in Language Model (LLM) systems for medical applications. The authors highlight that while previous research has primarily focused on mathematical tasks, domains like medicine require robust reasoning capabilities to provide reliable answers in healthcare settings. is crucial in healthcare as it involves complex decision-making processes that can have a significant impact on patient outcomes. However, have traditionally been limited in their ability to perform such reasoning tasks effectively. To address this issue, to ensure the correctness of model outputs. This  enables advancements in medical reasoning through a two-stage process. Firstly,. Secondly,. The authors introduce . Through experiments using 40K verifiable problems,< kd >they demonstrate that HuatuoGPT-o1 outperforms both general-purpose and medical-specific baselines</ kd >. The results show that < kd >complex reasoning significantly improves medical problem-solving and benefits greatly from RL techniques</ kd >. Overall,< kd >the authors hope that their innovative approach will inspire advancements in reasoning across various specialized domains beyond just medicine</ kd >. Their work sheds light on the importance of enhancing reasoning capabilities in LLM systems for tackling complex challenges in specialized fields such as healthcare.