---
title: "HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs"
description: "The approach offers a practical recipe for specialized domains that lack step-level supervision. 86 references, cited by 80 papers here."
authors: ["Junying Chen", "Zhenyang Cai", "Ke Ji", "Xidong Wang", "Wanlong Liu", "Rongsheng Wang", "Jianye Hou", "Benyou Wang"]
year: 2024
venue: "arXiv:2412.18925"
source: "https://arxiv.org/abs/2412.18925"
doi: "10.48550/arxiv.2412.18925"
arxiv: "2412.18925"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/"
---
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# HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

**Authors:** [Junying Chen](https://md.research.elamur.ai/free/en/authors/junying-chen/), [Zhenyang Cai](https://md.research.elamur.ai/free/en/authors/zhenyang-cai/), [Ke Ji](https://md.research.elamur.ai/free/en/authors/ke-ji/), [Xidong Wang](https://md.research.elamur.ai/free/en/authors/xidong-wang/), [Wanlong Liu](https://md.research.elamur.ai/free/en/authors/wanlong-liu/), Rongsheng Wang, [Jianye Hou](https://md.research.elamur.ai/free/en/authors/jianye-hou/) and [Benyou Wang](https://md.research.elamur.ai/free/en/authors/benyou-wang/) · **Year:** 2024 · **Venue:** arXiv:2412.18925 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2412.18925) · [PDF](https://arxiv.org/pdf/2412.18925) · [DOI 10.48550/arxiv.2412.18925](https://doi.org/10.48550/arxiv.2412.18925) · [arXiv 2412.18925](https://arxiv.org/abs/2412.18925) · [OpenAlex](https://openalex.org/W4405901456)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [80](https://md.research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Work that reproduces OpenAI o1-style reasoning has mostly targeted mathematics. Medicine also needs careful step-by-step reasoning, but a clinical chain of thought is hard to check automatically. The authors address this by building verifiable medical problems. They start from 192K multiple-choice exam questions, then remove items that are easy or ambiguous. GPT-4o turns the remaining questions into open-ended problems, each with a single reference answer, which yields a 40K-item set. A GPT-4o-based verifier judges whether a model's final answer matches the reference.

Training proceeds in two phases. First, the verifier guides a search for long reasoning traces: when an attempt is rejected, the model extends it using strategies such as backtracking or correction. The successful traces are then used for supervised fine-tuning. Second, PPO reinforcement learning uses the verifier's binary judgments as rewards.

The resulting HuatuoGPT-o1 models outperform open general-purpose and medical LLMs on medical benchmarks. The experiments also show that long reasoning makes RL more effective. The approach offers a practical recipe for specialized domains that lack step-level supervision.

## Key points
- **Source data:** questions come from the MedQA-USMLE and MedMCQA training sets. A question was discarded if Gemma2-9B, LLaMA-3.1-8B and Qwen2.5-7B all answered it correctly.
- **Results:** with only 40K problems, the 8B model gains 8.5 points on medical benchmarks. The 70B version beats open-source general and medical-specific LLMs.
- **Verifier quality:** on 200 hand-labeled samples, the GPT-4o verifier reached about 94.5–96.5% accuracy, while exact matching scored only 70.5–74.5%. A fine-tuned LLaMA-3.1-8B verifier also exceeded 90%.
- **Chinese transfer:** HuatuoGPT-o1-7B-zh was built on Qwen2.5-7B-Instruct using 40K CMB-exam problems. It scored 79.8 on MedQA (Chinese), 73.0 on CMB (Exam), 74.1 on CMExam and 74.5 on CMMLU (Med). Dropping the RL stage lowered each score by roughly 2–4 points.
- **Release:** code and models are published at github.com/FreedomIntelligence/HuatuoGPT-o1.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chinese University of Hong Kong, Shenzhen](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong-shenzhen/) — University, Shenzhen, China · 56 works · 3,212 citations
- [Shenzhen Research Institute of Big Data](https://md.research.elamur.ai/free/en/universities/shenzhen-research-institute-of-big-data/) — University, Shenzhen, China · 20 works · 2,016 citations

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🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works

## Cite

Cite as: Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou and Benyou Wang. 2024. HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs. arXiv:2412.18925.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{chen2024huatuogpt,
  title = {{HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}},
  author = {Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou and Benyou Wang},
  year = {2024},
  doi = {10.48550/arxiv.2412.18925},
  eprint = {2412.18925},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2412.18925}
}
```

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*Updated 2 November 2026*