DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

A recent study titled 'DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models' has been released on arXiv (ID: 2511.15669v3). This research explores whether Chain-of-Thought (CoT) reasoning truly enhances Vision-Language-Action (VLA) models or simply introduces additional complexity. The authors highlight two essential conditions for effective CoT: Decoding Alignment, which requires CoT and actions to be produced through modality-appropriate methods, and Causal Alignment, where CoT must be directly related to successful task outcomes via optimization based on results. They found that using a single autoregressive decoder for CoT and actions led to a 4.2 percentage point performance decline, and without causal alignment, supervised CoT performed similarly to no reasoning, resulting in a 32.0 percentage point drop during action-execution-sensitive dynamics shift, closely mirroring the 31.6 percentage point decline observed without CoT. The paper can be accessed at https://arxiv.org/abs/2511.15669.