arXiv:2605.29380v1 Announce Type: new Abstract: Mainstream strategies for finetuning pretrained multimodal models often degrade out-of-distribution (OOD) robustness, a phenomenon known as catastrophic forgetting. In this paper, we develop a theoretical framework for multimodal contrastive finetuning, yielding closed-form solutions and a geometric decomposition for each strategy. This framework shows that self-distillation is more effective than other regularization approaches to retain the knowledge of the pretrained model. Our analysis reveals a largely overlooked limitation: standard Exponen
The paper addresses the contemporary challenge of maintaining model robustness during finetuning, a critical issue as multimodal AI deployment expands.
Improved finetuning techniques that prevent catastrophic forgetting are crucial for developing reliable and adaptable AI systems, directly impacting the performance and longevity of AI deployments.
The theoretical framework and practical insights into self-distillation offer a more effective approach to preserve pretrained model knowledge, leading to more robust multimodal AI.
- · AI model developers
- · Multimodal AI applications
- · Robust AI research
- · Inefficient finetuning methods
- · AI systems prone to catastrophic forgetting
Multimodal models will become more reliable and performant in diverse real-world scenarios.
This improved robustness could accelerate the adoption of complex AI agents and autonomous systems requiring continuous adaptation.
Enhanced AI stability and generalization capabilities may reduce development costs and broaden access to sophisticated AI technologies.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG