Article

CondenseVLA: Improving Multi-Frame VLA Policy Efficiency with Learnable History Compression

CondenseVLA proposes adding a learnable history compression module to the decoding side of VLA policies. It uses learnable queries and multi-head cross-attention to distill the per-frame features cached by the VLM backbone into a fixed number of slot tokens, and applies auxiliary regularization that penalizes overlap among slot attention maps and decorrelates slot embeddings to reduce redundancy.

Part of the event: CondenseVLA: Learnable History Compression Improves Multi-Frame VLA Policies

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