Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching
Autoregressive Vision-Language-Action (VLA) models offer a scalable path to robot learning, yet existing action tokenizers treat tokenization as a compression problem, producing representations that are semantically misaligned with the autoregressive backbone. We propose CATok, a causal action tokenizer that reframes tokenization as a causally structured generative process. CATok introduces a conditional annealing mechanism that extracts action tokens by progressively annealing a flow-matching process: each token is conditioned on all preceding tokens and encodes the residual reconstruction signal at a specific noise level, establishing a coarse-to-fine causal token space whose generative...
| Authors | Chenyu Zhang, Yuhang Cao, Daru Du et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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