MiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling

Name: MiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling
Uploaded: 2026-10-11T19:31:38.832235+00:00
Duration: 785 s
Description: MiniWAM replaces costly native future prediction with compact predictive representations learned from current-to-future transitions. It uses PRISM to learn these small targets, showing that effective world-action modeling doesn't need full visual futures. This approach achieves up to 8x speedup in training and outperforms native methods.

MiniWAM replaces costly native future prediction with compact predictive representations learned from current-to-future transitions. It uses PRISM to learn these small targets, showing that effective world-action modeling doesn't need full visual futures. This approach achieves up to 8x speedup in training and outperforms native methods.

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