LogicVLA: Neural Logic Critics for Efficient Vision-Language-Action Fine-Tuning

Vision-language-action (VLA) models achieve strong performance in robotic manipulation, yet downstream adaptation remains computationally expensive. A fundamental limitation of standard VLA fine-tuning is its reliance on pointwise behavioral cloning, which penalizes numerical deviations from expert actions but provides no explicit supervision on whether predicted actions are compatible with the task instruction and current state. To solve this challenge, this paper develops LogicVLA, which is a training-time logical regularization framework that complements imitation learning with task-conditioned action consistency. Particularly, a lightweight neural logic consistency critic maps language instructions, visual observations, proprioceptive states, and actions into a shared latent space, composes their interactions through differentiable logic operators, and produces a consistency score that measures the compatibility between the multimodal task context and the predicted action. The critic contains 737K parameters and accounts for only 0.01% of the VLA backbone. It is trained directly from the original demonstrations by contrasting expert tuples with in-batch mismatched tuples, without requiring additional annotations or environment interaction. During fine-tuning, the learned consistency score provides an auxiliary regularization signal for predicted actions, while a dedicated stability mechanism prevents unstable critic gradients from affecting policy optimization. Extensive simulation results on LIBERO-Spatial show that, compared with OpenVLA, LogicVLA improves closed-loop success by 2.6% at 15K training steps and by 26% on the complex tasks, while introducing only 0.0096% additional trainable parameters during fine-tuning and no additional deployment-time computation. Code is available at https://anonymous.4open.science/r/LogicVLA-379E.