arXiv:2510.05681v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the model's internal properties without requir
The rapid advancement of Vision-Language-Action models necessitates increased precision and reliability for real-world robotic deployment, driving innovation in test-time scaling methods.
Improving the precision of VLA models without external verifiers could significantly accelerate the deployment of advanced robotics in applications requiring high accuracy, impacting various industries.
This research introduces a novel framework for enhancing VLA precision, potentially enabling more robust and generalized robotic control systems without the need for additional, condition-specific training.
- · Robotics companies
- · AI software developers
- · Logistics and manufacturing sectors
- · AI research institutions
- · Developers reliant on external verifiers
- · Industries with low precision tolerance
Increased reliability and broader application of VLA-controlled robots in complex tasks.
Accelerated commercialization and adoption of humanoid and general-purpose robots due to enhanced operational precision.
Reduced labor costs and increased automation across sectors, potentially leading to significant shifts in workforce demands and economic structures.
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Read at arXiv cs.AI