CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.g. OpenVLA) and continuous-action flow-matching (e.g. pi-0.5). Yet preference alignment via Direct Preference Optimisation (DPO) -- the de-facto post-training step in language models -- has been studied almost exclusively on autoregressive VLAs. We present CrossVLA, an empirical study of cross-paradigm VLA post-training. Three contributions: (i) a surrogate flow-matching log-probability estimator that lets DPO operate on continuous-action backbones without probability-flow ODE integration; (ii) a head-to-head comparison of LoRA and DoRA as the parameter-eff...
| Authors | Zhi Liu |
|---|---|
| Primary category | cs.CV |
| Published | 21 May 2026 |
| Last updated at source | 6 June 2026 |
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