Breaking the Resource Barrier: Parameter-Efficient Hierarchical VLA Fine-Tuning via Single-View Semantic Reasoning

Abstract

As Vision-Language-Action (VLA) models continue to scale in the number of parameters, the computational cost and resource requirements for domain-specific fine-tuning have become significant barriers to practical robotic deployment. While Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) offer a potential solution, they often fail to match the task success rates of their fully fine-tuned counterparts. In this paper, we propose a novel hierarchical VLA architecture that achieves state-of-the-art performance while maintaining high parameter efficiency. Our model decomposes control into a high-level System 2 for instruction-conditioned semantic context encoding—a frozen PaliGemma-3B backbone with 0.12B trainable LoRA parameters and a reactive System 1 for multimodal fusion and action generation. To optimize training efficiency, System 2 takes only a single egocentric image, while System 1 recovers missing context by integrating wrist-view images via ResNet-34 and proprioceptive state history encoded with a single linear projection layer. This information is fused through a Transformer Encoder, and final action trajectories are refined via a Transformer-parameterized conditional flow-matching decoder. To improve task performance, we generate diverse candidates by sampling from N independently initialized Gaussian noise vectors and using different numbers of denoising steps K per sample, and then select the executed action using a Cal-QL (Calibrated Q-Learning)-based critic. Evaluated on the standardized LIBERO benchmark, our proposed model achieved a 98.1% average success rate, outperforming contemporary fully trained models across all task suites. These results demonstrate that strategic architectural design can enable parameter-efficient models to exceed the performance of full-scale fine-tuning, offering a viable path for high-performance robotics under constrained computational resources.