# TacitVLA

Reasoning vision-language-action models previously made every action wait for autoregressive decoding of the reasoning text, which accounted for 53% to 70% of per-call latency. TacitVLA masks the action expert's attention to the current reasoning tokens, keeping only the training effect of reasoning supervision on the backbone representation, and on memory tasks generates each ledger entry during action execution. Across RoboMME, RMBench, LIBERO and PhysicalAI-AV it matches its explicit-reasoning counterpart while cutting latency up to 3.4x, and matches no-reasoning latency while improving success by up to 17.1%.

Reasoning vision-language-action models previously made every action wait for autoregressive decoding of the reasoning text, which accounted for 53% to 70% of per-call latency. TacitVLA masks the action expert's attention to the current reasoning tokens, keeping only the training effect of reasoning supervision on the backbone representation, and on memory tasks generates each ledger entry during action execution. Across RoboMME, RMBench, LIBERO and PhysicalAI-AV it matches its explicit-reasoning counterpart while cutting latency up to 3.4x, and matches no-reasoning latency while improving success by up to 17.1%.