Long-WAM: Scaling the Context of World-Action Models
Long-WAM is presented, a model-system framework for scaling the context of causal world-action models under real-time control constraints, and finds that access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively (AR).
Wei Huang, Bo-Han Zhang, Chen-Zhi Liu et al.
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