TRACER: Persistent Regularization for Robust Multimodal Finetuning

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 these strategies. 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 Exponential Moving Average (EMA) teachers, widely used in robust finetuning, suffer from collapse. To solve this, we prove that a Weighted Moving Average (WMA) teacher maintains a persistent regularizing force over finite horizons and yields bias-free convergence in the task subspace while preserving orthogonal knowledge. These insights motivate TRACER (Trajectory-Robust Anchoring for Contrastive Encoder Regularization), which combines contrastive learning with WMA-guided multi-perspective distillation. Extensive experiments on CLIP finetuning demonstrate consistent OOD accuracy and calibration gains across three backbone architectures, and comprehensive ablations confirm that TRACER is both principled and robust to hyperparameter choices. Code is available at https://github.com/HesamAsad/TRACER.

When we teach a powerful AI model a specific new task, it often forgets its general knowledge—a problem called "catastrophic forgetting." The AI becomes excellent at its new job but fails at things it used to know. We discovered that standard methods used to prevent this forgetting break down over time. To fix this, we created TRACER, a new approach that acts like a steady anchor. It constantly reminds the AI of its core knowledge while it learns new skills. Our tests show that TRACER allows AI systems to master new tasks while keeping their original, broad understanding, making them much more reliable when facing unexpected data.