The Compression Gap: Why Discrete Tokenization Limits Vision-Language-Action Model Scaling
Scaling Vision-Language-Action (VLA) models by upgrading the vision encoder is expected to improve downstream manipulation performance--as it does in vision-language modeling. We show that this expectation fails when actions are represented as discrete tokens, and explain why through an information-theoretic principle we call the Compression Gap: in any visuomotor pipeline, scaling behavior is governed by the location of the tightest information bottleneck. When actions are continuous (e.g., Diffusion Policy), the vision encoder is the binding constraint, and upgrading it directly improves performance. When actions are discretized through a fixed-capacity codebook (e.g., OAT), the codeboo...
| Authors | Takuya Shiba |
|---|---|
| Primary category | cs.RO |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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