@inproceedings{xu-etal-2026-comfyui,
title = "{C}omfy{UI}-R1: Exploring Reasoning Models for Workflow Generation",
author = "Xu, Zhenran and
Wang, Yiyu and
Yangxue and
Wang, Longyue and
Luo, Weihua and
Zhang, Kaifu and
Hu, Baotian and
Zhang, Min",
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.146/",
pages = "2999--3013",
ISBN = "979-8-89176-395-1",
abstract = "AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated knowledge bases, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97{\%} format validity rate, along with high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Qualitative analysis further highlights our model{'}s strength in synthesizing intricate workflows with diverse nodes, aligning with human instructions, and generalizing to newly introduced nodes, underscoring the potential of long CoT reasoning in AI art creation."
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<abstract>AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated knowledge bases, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97% format validity rate, along with high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Qualitative analysis further highlights our model’s strength in synthesizing intricate workflows with diverse nodes, aligning with human instructions, and generalizing to newly introduced nodes, underscoring the potential of long CoT reasoning in AI art creation.</abstract>
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%0 Conference Proceedings
%T ComfyUI-R1: Exploring Reasoning Models for Workflow Generation
%A Xu, Zhenran
%A Wang, Yiyu
%A Wang, Longyue
%A Luo, Weihua
%A Zhang, Kaifu
%A Hu, Baotian
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Yangxue
%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 xu-etal-2026-comfyui
%X AI-generated content has evolved from monolithic models to modular workflows, particularly on platforms like ComfyUI, enabling customization in creative pipelines. However, crafting effective workflows requires great expertise to orchestrate numerous specialized components, presenting a steep learning curve for users. To address this challenge, we introduce ComfyUI-R1, the first large reasoning model for automated workflow generation. Starting with our curated knowledge bases, we construct long chain-of-thought (CoT) reasoning data, including node selection, workflow planning, and code-level workflow representation. ComfyUI-R1 is trained through a two-stage framework: (1) CoT fine-tuning for cold start, adapting models to the ComfyUI domain; (2) reinforcement learning for incentivizing reasoning capability, guided by a fine-grained rule-metric hybrid reward, ensuring format validity, structural integrity, and node-level fidelity. Experiments show that our 7B-parameter model achieves a 97% format validity rate, along with high pass rate, node-level and graph-level F1 scores, surpassing prior state-of-the-art methods that employ leading closed-source models such as GPT-4o and Claude series. Qualitative analysis further highlights our model’s strength in synthesizing intricate workflows with diverse nodes, aligning with human instructions, and generalizing to newly introduced nodes, underscoring the potential of long CoT reasoning in AI art creation.
%U https://aclanthology.org/2026.findings-acl.146/
%P 2999-3013
Markdown (Informal)
[ComfyUI-R1: Exploring Reasoning Models for Workflow Generation](https://aclanthology.org/2026.findings-acl.146/) (Xu et al., Findings 2026)
ACL
- Zhenran Xu, Yiyu Wang, Yangxue, Longyue Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, and Min Zhang. 2026. ComfyUI-R1: Exploring Reasoning Models for Workflow Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2999–3013, San Diego, California, United States. Association for Computational Linguistics.