SLA: Scaling Spatial-Language-Action Model via Continual Pre-training
The paper presents SLA, a Spatial-Language-Action model that continually pre-trains and post-trains a pretrained pi05 VLA policy.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourcehttps://github.com/OpenHelix-Team/Spatial-Forcing
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The paper presents SLA, a Spatial-Language-Action model that continually pre-trains and post-trains a pretrained pi05 VLA policy. It jointly optimizes action prediction and alignment between intermediate visual embeddings and spatial representations from a frozen 3D perception model. Feature-dependent gates aggregate visual embeddings across VLA layers, and probabilistic state dropout reduces overfitting to demonstration trajectories. Auxiliary components are used only in training, so pretrained VLA weights and the inference architecture are preserved. The supplied text is a page abstract plus an author comment, and the full paper was not checked.
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SLA initializes from a pretrained pi05 policy and aligns intermediate visual embeddings with a frozen 3D perception model.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseFeature-dependent gates adaptively aggregate visual embeddings from multiple VLA layers for spatial supervision.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseProbabilistic state dropout is used to encourage reliance on visual cues and reduce overfitting to demonstrations.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Adding spatial understanding during pre-training, rather than only during task adaptation, could make robot manipulation foundations more transferable. Keeping auxiliary components training-only means existing VLA weights can be reused directly.
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