ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

The Compressor-VLA framework has been introduced as an innovative solution for the challenges faced by Vision-Language-Action (VLA) models in robotic manipulation, specifically targeting the inefficiencies caused by redundant visual tokens. This hybrid instruction-conditioned token compression framework includes two modules: the Semantic Task Compressor and the Spatial Refinement Compressor, aimed at preserving both holistic context and fine-grained details.

The KV-Efficient VLA introduces a model-agnostic memory compression technique aimed at enhancing the efficiency of Vision-Language-Action (VLA) models by utilizing a recurrent gating module to selectively retain high-utility context during inference. This method addresses the computational challenges posed by traditional attention mechanisms and the extensive memory requirements for key-value pairs, particularly in long-horizon tasks.

The Evo-0 model has been introduced as a Vision-Language-Action (VLA) framework that enhances spatial understanding by integrating implicit 3D geometry features. This advancement addresses the limitations of existing Vision-Language Models (VLMs), which often lack precise spatial reasoning due to their reliance on 2D image-text pairs without 3D supervision.

AVA-VLA is a newly proposed framework aimed at enhancing Vision-Language-Action (VLA) models by integrating Active Visual Attention (AVA) to improve visual processing in dynamic decision-making contexts. This approach addresses the limitations of traditional VLA models that operate independently at each timestep, which can hinder effective contextual understanding in sequential tasks.

VLA-Pruner has been introduced as a novel method for token pruning in Vision-Language-Action (VLA) models, addressing the inefficiencies of existing approaches that focus solely on semantic salience. This method aims to enhance real-time deployment of VLA models by retaining critical information necessary for action generation while discarding redundant visual tokens.