Position: Iterative Online-Offline Joint Optimization is Needed to Manage Complex LLM Copyright Risks

Yanzhou Pan, Jiayi Chen, Jiamin Chen, Zhaozhuo Xu, Denghui Zhang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81962-81976, 2025.

Abstract

The infringement risks of LLMs have raised significant copyright concerns across different stages of the model lifecycle. While current methods often address these issues separately, this position paper argues that the LLM copyright challenges are inherently connected, and independent optimization of these solutions leads to theoretical bottlenecks. Building on this insight, we further argue that managing LLM copyright risks requires a systemic approach rather than fragmented solutions. In this paper, we analyze the limitations of existing methods in detail and introduce an iterative online-offline joint optimization framework to effectively manage complex LLM copyright risks. We demonstrate that this framework offers a scalable and practical solution to mitigate LLM infringement risks, and also outline new research directions that emerge from this perspective.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-pan25i, title = {Position: Iterative Online-Offline Joint Optimization is Needed to Manage Complex {LLM} Copyright Risks}, author = {Pan, Yanzhou and Chen, Jiayi and Chen, Jiamin and Xu, Zhaozhuo and Zhang, Denghui}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81962--81976}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/pan25i/pan25i.pdf}, url = {https://proceedings.mlr.press/v267/pan25i.html}, abstract = {The infringement risks of LLMs have raised significant copyright concerns across different stages of the model lifecycle. While current methods often address these issues separately, this position paper argues that the LLM copyright challenges are inherently connected, and independent optimization of these solutions leads to theoretical bottlenecks. Building on this insight, we further argue that managing LLM copyright risks requires a systemic approach rather than fragmented solutions. In this paper, we analyze the limitations of existing methods in detail and introduce an iterative online-offline joint optimization framework to effectively manage complex LLM copyright risks. We demonstrate that this framework offers a scalable and practical solution to mitigate LLM infringement risks, and also outline new research directions that emerge from this perspective.} }
Endnote
%0 Conference Paper %T Position: Iterative Online-Offline Joint Optimization is Needed to Manage Complex LLM Copyright Risks %A Yanzhou Pan %A Jiayi Chen %A Jiamin Chen %A Zhaozhuo Xu %A Denghui Zhang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-pan25i %I PMLR %P 81962--81976 %U https://proceedings.mlr.press/v267/pan25i.html %V 267 %X The infringement risks of LLMs have raised significant copyright concerns across different stages of the model lifecycle. While current methods often address these issues separately, this position paper argues that the LLM copyright challenges are inherently connected, and independent optimization of these solutions leads to theoretical bottlenecks. Building on this insight, we further argue that managing LLM copyright risks requires a systemic approach rather than fragmented solutions. In this paper, we analyze the limitations of existing methods in detail and introduce an iterative online-offline joint optimization framework to effectively manage complex LLM copyright risks. We demonstrate that this framework offers a scalable and practical solution to mitigate LLM infringement risks, and also outline new research directions that emerge from this perspective.
APA
Pan, Y., Chen, J., Chen, J., Xu, Z. & Zhang, D.. (2025). Position: Iterative Online-Offline Joint Optimization is Needed to Manage Complex LLM Copyright Risks. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81962-81976 Available from https://proceedings.mlr.press/v267/pan25i.html.

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