Modality-Autoregressive World-Action Models
ModAR is a World-Action Model that predicts future observations as multiple modalities by autoregressively denoising them sequentially before predicting actions. It outperforms existing models by conditioning each prediction on previously generated modalities, achieving the highest success rates across data scales and showing improvements with actionless data.