MiniWAM: Learning Compact Future Targets for Efficient World
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: MiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling. MiniWAM is a new method for world-action modeling that predicts compact future representations instead of high-dimensional future features. By using PRISM, which combines inverse-dynamics supervision with feature reconst…
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · future feature tokens: 65 other · Basis not reported
“coder frozen, MiniWAM is trained to jointly predict the resulting targets and robot actions from current observations. With 65$\times$ fewer native future feature tokens, MiniWAM consistently outperforms native future-feature prediction with both DINOv3 and WAN2.1 VAE features, while achieving up to an 8$\times$ speedup in world-action training. At 0.25B parameters, MiniWAM is already competitive with substantially l”
Exact source · revision 1Source owner not reported
Reported quantity · speedup: 8 other · Basis not reported
“coder frozen, MiniWAM is trained to jointly predict the resulting targets and robot actions from current observations. With 65$\times$ fewer native future feature tokens, MiniWAM consistently outperforms native future-feature prediction with both DINOv3 and WAN2.1 VAE features, while achieving up to an 8$\times$ speedup in world-action training. At 0.25B parameters, MiniWAM is already competitive with substantially l”
Exact source · revision 1Source owner not reported
Report timeline
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- arXiv Robotics — research abstractsMiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling
MiniWAM is a new method for world-action modeling that predicts compact future representations instead of high-dimensional future features. By using PRISM, which combines inverse-dynamics supervision with feature reconstruction, MiniWAM achieves significant efficiency gains and outperforms existing methods in simulation benchmarks with fewer parameters.
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