@inproceedings{yu-etal-2026-experience,
title = "Experience is the Teacher: Reusing Atomic Thoughts from {LLM}s to Improve Medical Dialogue",
author = "Yu, Guangya and
Luo, Hui and
Ye, Qi and
Hou, Ruihui and
Zhang, Weiyan and
Shang, Mingxi and
Li, Xuanwu and
Wang, ChunMing and
Ruan, Tong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.957/",
pages = "19165--19184",
ISBN = "979-8-89176-395-1",
abstract = "With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and diverse nature of real-world multi-turn conversations. While recent large reasoning models (such as R1) enable deeper and more complex thought processes to address such challenges, they also introduce significant redundancy. Meanwhile, recent studies on reusing atomic thoughts demonstrate a practical pathway toward dynamic and precise reasoning in general domains. In this paper, we investigate the role of atomic thought-based experience in medical dialogue tasks. First, we collect human expert clinical experience. Then, we propose a novel distillation framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. Based on this framework, we construct training data from ReMeDi and fine-tune student models, which demonstrate enhanced performance in both static and interactive medical dialogue scenarios. Furthermore, we examine the impact of experience across various models, datasets, and scenarios. Crucially, transferring this experience empowers weaker models to generate high-quality reasoning data, matching the annotation capabilities of stronger LLMs while significantly reducing costs. The code is available in this repository https://github.com/VioletAmethystLunar/Atomic-Thoughts-Medical-Dialogue."
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<abstract>With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and diverse nature of real-world multi-turn conversations. While recent large reasoning models (such as R1) enable deeper and more complex thought processes to address such challenges, they also introduce significant redundancy. Meanwhile, recent studies on reusing atomic thoughts demonstrate a practical pathway toward dynamic and precise reasoning in general domains. In this paper, we investigate the role of atomic thought-based experience in medical dialogue tasks. First, we collect human expert clinical experience. Then, we propose a novel distillation framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. Based on this framework, we construct training data from ReMeDi and fine-tune student models, which demonstrate enhanced performance in both static and interactive medical dialogue scenarios. Furthermore, we examine the impact of experience across various models, datasets, and scenarios. Crucially, transferring this experience empowers weaker models to generate high-quality reasoning data, matching the annotation capabilities of stronger LLMs while significantly reducing costs. The code is available in this repository https://github.com/VioletAmethystLunar/Atomic-Thoughts-Medical-Dialogue.</abstract>
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%0 Conference Proceedings
%T Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue
%A Yu, Guangya
%A Luo, Hui
%A Ye, Qi
%A Hou, Ruihui
%A Zhang, Weiyan
%A Shang, Mingxi
%A Li, Xuanwu
%A Wang, ChunMing
%A Ruan, Tong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yu-etal-2026-experience
%X With the remarkable performance of large language models (LLMs) in medicine, particularly their ability to support clinical decision-making in medical dialogues, a key limitation remains: the static reasoning patterns derived from human expert experience are often inadequate for the dynamic and diverse nature of real-world multi-turn conversations. While recent large reasoning models (such as R1) enable deeper and more complex thought processes to address such challenges, they also introduce significant redundancy. Meanwhile, recent studies on reusing atomic thoughts demonstrate a practical pathway toward dynamic and precise reasoning in general domains. In this paper, we investigate the role of atomic thought-based experience in medical dialogue tasks. First, we collect human expert clinical experience. Then, we propose a novel distillation framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. Based on this framework, we construct training data from ReMeDi and fine-tune student models, which demonstrate enhanced performance in both static and interactive medical dialogue scenarios. Furthermore, we examine the impact of experience across various models, datasets, and scenarios. Crucially, transferring this experience empowers weaker models to generate high-quality reasoning data, matching the annotation capabilities of stronger LLMs while significantly reducing costs. The code is available in this repository https://github.com/VioletAmethystLunar/Atomic-Thoughts-Medical-Dialogue.
%U https://aclanthology.org/2026.findings-acl.957/
%P 19165-19184
Markdown (Informal)
[Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue](https://aclanthology.org/2026.findings-acl.957/) (Yu et al., Findings 2026)
ACL
- Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, and Tong Ruan. 2026. Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19165–19184, San Diego, California, United States. Association for Computational Linguistics.