@inproceedings{zhao-etal-2026-vincicoder,
title = "{V}inci{C}oder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning",
author = "Zhao, Xuanle and
Jiang, Deyang and
Zeng, Zhixiong and
Chen, Lei and
Yang, Haoyue and
Qiu, Haibo and
Huang, Jing and
Zhong, Yufeng and
Zheng, Liming and
Cao, Yilin and
Ma, Lin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2266/",
pages = "51529--51544",
ISBN = "979-8-89176-459-0",
abstract = "While recent specialized multimodal code generation models excel in tasks like chart-to-code generation, their reliance on single-task training limits generalization and hinders the development of \textbf{VI}sio\textbf{N} \textbf{C}ode \textbf{I}ntelligence. In this work, we introduce \textbf{VinciCoder}, a unified framework designed for generalized multimodal code generation. We first curate a large-scale SFT corpus comprising 1.3M direct generation pairs and 300k visual-based refinement tasks. This composition fosters self-refinement capabilities, enabling the model to directly rectify code to align with input images. Subsequently, we propose coarse-to-fine Visual Reinforcement Learning (ViRL) to overcome the brittleness of textual metrics in handling semantically equivalent but syntactically diverse code. By quantifying visual similarity across multi-scale patches, ViRL provides an implementation-agnostic reward mechanism that ensures high-fidelity alignment between rendered outputs and input visuals. Extensive experimental results across diverse benchmarks demonstrate that VinciCoder achieves superior performance, while comprehensive ablation studies validate the effectiveness of our proposed ViRL strategy. The data, code and model are available at \url{https://github.com/DocTron-hub/VinciCoder}."
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<abstract>While recent specialized multimodal code generation models excel in tasks like chart-to-code generation, their reliance on single-task training limits generalization and hinders the development of VIsioN Code Intelligence. In this work, we introduce VinciCoder, a unified framework designed for generalized multimodal code generation. We first curate a large-scale SFT corpus comprising 1.3M direct generation pairs and 300k visual-based refinement tasks. This composition fosters self-refinement capabilities, enabling the model to directly rectify code to align with input images. Subsequently, we propose coarse-to-fine Visual Reinforcement Learning (ViRL) to overcome the brittleness of textual metrics in handling semantically equivalent but syntactically diverse code. By quantifying visual similarity across multi-scale patches, ViRL provides an implementation-agnostic reward mechanism that ensures high-fidelity alignment between rendered outputs and input visuals. Extensive experimental results across diverse benchmarks demonstrate that VinciCoder achieves superior performance, while comprehensive ablation studies validate the effectiveness of our proposed ViRL strategy. The data, code and model are available at https://github.com/DocTron-hub/VinciCoder.</abstract>
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%0 Conference Proceedings
%T VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning
%A Zhao, Xuanle
%A Jiang, Deyang
%A Zeng, Zhixiong
%A Chen, Lei
%A Yang, Haoyue
%A Qiu, Haibo
%A Huang, Jing
%A Zhong, Yufeng
%A Zheng, Liming
%A Cao, Yilin
%A Ma, Lin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhao-etal-2026-vincicoder
%X While recent specialized multimodal code generation models excel in tasks like chart-to-code generation, their reliance on single-task training limits generalization and hinders the development of VIsioN Code Intelligence. In this work, we introduce VinciCoder, a unified framework designed for generalized multimodal code generation. We first curate a large-scale SFT corpus comprising 1.3M direct generation pairs and 300k visual-based refinement tasks. This composition fosters self-refinement capabilities, enabling the model to directly rectify code to align with input images. Subsequently, we propose coarse-to-fine Visual Reinforcement Learning (ViRL) to overcome the brittleness of textual metrics in handling semantically equivalent but syntactically diverse code. By quantifying visual similarity across multi-scale patches, ViRL provides an implementation-agnostic reward mechanism that ensures high-fidelity alignment between rendered outputs and input visuals. Extensive experimental results across diverse benchmarks demonstrate that VinciCoder achieves superior performance, while comprehensive ablation studies validate the effectiveness of our proposed ViRL strategy. The data, code and model are available at https://github.com/DocTron-hub/VinciCoder.
%U https://aclanthology.org/2026.emnlp-main.2266/
%P 51529-51544
Markdown (Informal)
[VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.2266/) (Zhao et al., EMNLP 2026)
ACL
- Xuanle Zhao, Deyang Jiang, Zhixiong Zeng, Lei Chen, Haoyue Yang, Haibo Qiu, Jing Huang, Yufeng Zhong, Liming Zheng, Yilin Cao, and Lin Ma. 2026. VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51529–51544, Budapest, Hungary. Association for Computational Linguistics.