SaiVLA-JEPA: Evaluating Future Latent Prediction in JEPA-Conditioned VLA Control

SaiVLA-JEPA is a family of vision‑language‑action policies that condition on frozen V‑JEPA features and use lightweight auxiliary heads to predict future latent representations during training. Experiments on 10,030 LIBERO‑Plus episodes show that JEPA conditioning improves success rates to about 82.5% versus 71.6% without JEPA, but adding future supervision does not yield a detectable aggregate gain. Variants that adapt latent context or selected parameters provide mixed results, suggesting that latent‑prediction objectives should be evaluated via closed‑loop rollouts rather than latent loss alone.

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• llms
• rag
• diffusion
• multimodal