Foundation models are redefining how AI systems are built. Practitioners now
follow a standard procedure to build their machine learning solutions: from a
pre-trained foundation model, they fine-tune the weights on the target task of
interest. So, the Internet is swarmed by a handful of foundation models
fine-tuned on many diverse tasks: these individual fine-tunings exist in
isolation without benefiting from each other. In our opinion, this is a missed
opportunity, as these specialized models contain rich and diverse features. In
this paper, we thus propose model ratatouille, a new strategy to recycle the
multiple fine-tunings of the same foundation model on diverse auxiliary tasks.
Specifically, we repurpose these auxiliary weights as initializations for
multiple parallel fine-tunings on the target task; then, we average all
fine-tuned weights to obtain the final model. This recycling strategy aims at
maximizing the diversity in weights by leveraging the diversity in auxiliary
tasks. Empirically, it improves the state of the art on the reference DomainBed
benchmark for out-of-distribution generalization. Looking forward, this work
contributes to the emerging paradigm of updatable machine learning where, akin
to open-source software development, the community collaborates to reliably
update machine learning models.

本文提出一种称为 Model Ratatouille 的新技术，通过在多个辅助任务上重新使用同一基础模型的多个微调，利用微调之间的差异提高结果多样性以优化目标任务结果。通过该技术在参考 DomainBed 基准上显着改善了结果，并将贡献到可更新机器学习的新兴范例和开源软件开发类似的社区合作。