💡 This paper proposes a novel approach called CapVector to improve the performance of vision-language-action models. The problem addressed is that pre-trained models often fail to improve performance and reduce adaptation costs during standard supervised finetuning. Advanced finetuning methods with auxiliary training objectives can improve performance but incur significant computational overhead.

The proposed method decouples the auxiliary training objectives from standard supervised finetuning to enhance model capabilities while reducing computational overhead. This is achieved by training the model to converge on a small-scale task set using two distinct training strategies, resulting in two finetuned models. The parameters difference between the two models is interpreted as capability vectors provided by auxiliary objectives. These vectors are then merged with pre-trained parameters to form a capability-enhanced meta model.

The method also uses a lightweight orthogonal regularization loss to augment standard supervised finetuning, which reduces computational overhead. The results show that the capability vectors are effective and versatile across diverse models, and can generalize to novel environments and embodiments without additional training. The proposed approach achieves performance comparable to auxiliary finetuned baselines with reduced computational overhead, making it a promising solution for improving vision-language-action models.