Fast LeWorldModel
- Published
- Source
- arXiv
- Paper number
- 506
- Field
- Machine Learning
- arXiv ID
- 2606.26217
Key points
- Action-prefix prediction generates multi-horizon latents in parallel, removing the bottleneck of sequential rollouts.
- The dynamics module time drops from 31.4 seconds to 8.0 seconds, a 3.9x speedup, and overall CEM solve time falls by 48%.
- Both the initial open-loop latent loss and its growth slope are significantly lower than in LeWM.
- Average planning success rate rises from 85.8% to 90.5%, including 98% on Two-Room, 96% on Push-T, and 80% on Cube.
- An ablation confirms that dense prefix supervision is essential for performance, not just terminal-only prediction.
- In physical-state probing, an MLP probe achieves the best accuracy on agent position, block position, and angle.
Paper links
External research summaries. These are not HDATF publications or measured product results.