BFA++: Hierarchical Best-Feature-Aware Token Prune for Multi-View Vision Language Action Model

TL;DR

BFA++ is a hierarchical, task-aware token pruning framework for multi-view vision-language action models that enhances efficiency and success rates in robotic manipulation tasks.

Contribution

It introduces a hierarchical, dynamic token pruning method tailored for VLA models, improving efficiency and accuracy over existing techniques.

Findings

BFA++ achieves about 10% higher success rates on RoboTwin benchmark.

It speeds up inference by 1.8X and 1.5X on different models.

The method effectively reduces cross-view redundancy and spatial noise.

Abstract

Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. However, the substantial increase in visual tokens, particularly from multi-view inputs, poses serious challenges to real-time robotic manipulation. Existing acceleration techniques for VLMs, such as token pruning, often result in degraded performance when directly applied to VLA models, as they overlook the relationships between different views and fail to account for the dynamic and task-specific characteristics of robotic operation. To address this, we propose BFA++, a dynamic token pruning framework designed specifically for VLA models. BFA++ introduces a hierarchical pruning strategy guided by two-level importance predictors: an intra-view predictor highlights task-relevant regions within each image to suppress spatial noise, while an inter-view predictor identifies critical camera views throughout different manipulation phases to reduce cross-view redundancy. This design enables efficient token selection while preserving essential visual cues, resulting in improved computational efficiency and higher manipulation success rates. Evaluations on the RoboTwin benchmark and real-world robotic tasks demonstrate that BFA++ consistently outperforms existing methods. BFA++ improves the success rate by about 10% on both the {\pi}0 and RDT models, achieving speedup of 1.8X and 1.5X, respectively. Our results highlight that context-sensitive and task-aware token pruning serves as a more effective strategy than full visual processing, enabling faster inference and improved manipulation accuracy in real-world robotic systems.