# Implicit Spatial Alignment

SLA extends spatial alignment for vision-language-action models from task post-training into continual pre-training, and keeps it during downstream post-training. Initialized from a pretrained policy, it jointly optimizes action prediction and a cosine alignment between intermediate visual embeddings and spatial representations from a frozen 3D perception model, using feature-dependent gates to aggregate multiple VLA layers and probabilistic state dropout to reduce reliance on demonstration trajectories. It reaches 72.3% on the RoboTwin 2.0 Clean2Random setting and 83.8% average success across eight real deployment conditions, 24.0 percentage points above the baseline.

SLA extends spatial alignment for vision-language-action models from task post-training into continual pre-training, and keeps it during downstream post-training. Initialized from a pretrained policy, it jointly optimizes action prediction and a cosine alignment between intermediate visual embeddings and spatial representations from a frozen 3D perception model, using feature-dependent gates to aggregate multiple VLA layers and probabilistic state dropout to reduce reliance on demonstration trajectories. It reaches 72.3% on the RoboTwin 2.0 Clean2Random setting and 83.8% average success across eight real deployment conditions, 24.0 percentage points above the baseline.