ConceptTune: Tuning Concept Vectors for Generalizable Video Understanding

Despite their impressive capabilities, multimodal large language models (MLLMs) still face challenges in video understanding, which current approaches mainly address through parameter fine-tuning, represented by LoRA and LOFIT, or activation steering, represented by RepE. The former enables accurate task-specific adjustment by updating model weights or parameter offsets, but may alter pretrained capabilities. The latter offers flexible generalization through direct representation-space intervention, but their positive–negative activation contrasts may not accurately represent the target concept. We propose ConceptTune, a hybrid method combining the accuracy of fine-tuning with the generalization ability of activation steering. ConceptTune fine-tunes a learnable concept offset directly in model activations rather than model parameters, treating this offset as the concept. Each concept offset is learned from many examples through concept-targeted generative completion. This training procedure encourages the concept offset to capture semantic information shared across training examples of the same concept type, yielding a reusable concept representation that can be applied as a lightweight, plug-and-play activation intervention at inference time. Experiments show that ConceptTune improves transfer across video-grounding benchmarks and compares favorably with parameter-efficient fine-tuning and contrastive activation steering, supporting concept-targeted learning in activation space.