SOURCE-LINKED INTELLIGENCE
Rollout-Decoded Reconstruction for Long-Horizon Prediction in Latent World Models
A latent world model trains its decoder on latents anchored to observations, then deploys it on the model's own free-running rollout, hundreds of steps past the last observation. Rollout-Decoded Reconstruction (RDR) closes this gap with a single loss term that free-runs the model during training exactly as evaluation will, decodes every rollout latent, and penalizes reconstruction error against ground truth. The term adds no parameters, costs training-time compute only, and reduces to the standard objective at weight zero, so every comparison in this paper is a one-flag A/B. On the chaotic Kur
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T18:05:24.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.