Learnware Specification via Dual Alignment

Wei Chen, Jun-Xiang Mao, Xiaozheng Wang, Min-Ling Zhang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:8683-8699, 2025.

Abstract

The learnware paradigm aims to establish a learnware dock system that contains numerous leanwares, each consisting of a well-trained model and a specification, enabling users to reuse high-performing models for their tasks instead of training from scratch. The specification, as a unique characterization of the model’s specialties, dominates the effectiveness of model reuse. Existing specification methods mainly employ distribution alignment to generate specifications. However, this approach overlooks the model’s discriminative performance, hindering an adequate specialty characterization. In this paper, we claim that it is beneficial to incorporate such discriminative performance for high-quality specification generation. Accordingly, a novel specification approach named Dali, i.e., Learnware Specification via Dual ALIgnment, is proposed. In Dali, the characterization of the model’s discriminative performance is modeled as discriminative alignment, which is considered along with distribution alignment in the specification generation process. Theoretical and empirical analyses clearly demonstrate that the proposed approach is capable of facilitating model reuse in the learnware paradigm with high-quality specification generation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-chen25as, title = {Learnware Specification via Dual Alignment}, author = {Chen, Wei and Mao, Jun-Xiang and Wang, Xiaozheng and Zhang, Min-Ling}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {8683--8699}, 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/chen25as/chen25as.pdf}, url = {https://proceedings.mlr.press/v267/chen25as.html}, abstract = {The learnware paradigm aims to establish a learnware dock system that contains numerous leanwares, each consisting of a well-trained model and a specification, enabling users to reuse high-performing models for their tasks instead of training from scratch. The specification, as a unique characterization of the model’s specialties, dominates the effectiveness of model reuse. Existing specification methods mainly employ distribution alignment to generate specifications. However, this approach overlooks the model’s discriminative performance, hindering an adequate specialty characterization. In this paper, we claim that it is beneficial to incorporate such discriminative performance for high-quality specification generation. Accordingly, a novel specification approach named Dali, i.e., Learnware Specification via Dual ALIgnment, is proposed. In Dali, the characterization of the model’s discriminative performance is modeled as discriminative alignment, which is considered along with distribution alignment in the specification generation process. Theoretical and empirical analyses clearly demonstrate that the proposed approach is capable of facilitating model reuse in the learnware paradigm with high-quality specification generation.} }
Endnote
%0 Conference Paper %T Learnware Specification via Dual Alignment %A Wei Chen %A Jun-Xiang Mao %A Xiaozheng Wang %A Min-Ling 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-chen25as %I PMLR %P 8683--8699 %U https://proceedings.mlr.press/v267/chen25as.html %V 267 %X The learnware paradigm aims to establish a learnware dock system that contains numerous leanwares, each consisting of a well-trained model and a specification, enabling users to reuse high-performing models for their tasks instead of training from scratch. The specification, as a unique characterization of the model’s specialties, dominates the effectiveness of model reuse. Existing specification methods mainly employ distribution alignment to generate specifications. However, this approach overlooks the model’s discriminative performance, hindering an adequate specialty characterization. In this paper, we claim that it is beneficial to incorporate such discriminative performance for high-quality specification generation. Accordingly, a novel specification approach named Dali, i.e., Learnware Specification via Dual ALIgnment, is proposed. In Dali, the characterization of the model’s discriminative performance is modeled as discriminative alignment, which is considered along with distribution alignment in the specification generation process. Theoretical and empirical analyses clearly demonstrate that the proposed approach is capable of facilitating model reuse in the learnware paradigm with high-quality specification generation.
APA
Chen, W., Mao, J., Wang, X. & Zhang, M.. (2025). Learnware Specification via Dual Alignment. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:8683-8699 Available from https://proceedings.mlr.press/v267/chen25as.html.

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