Training to improve planning reliability and extend the effective horizon in video diffusion models

Determine whether training approaches can produce video diffusion models that plan more reliably and over longer horizons than currently observed, overcoming the sharp failure cliff on trajectories exceeding the single-generation window.

Background

The study demonstrates that path length, not obstacle density, is the dominant driver of maze difficulty, with a marked failure threshold at approximately 12 steps. This suggests that current models are limited by an effective generation horizon, motivating chaining across multiple generations to solve longer tasks.

While inference-time strategies (Early Planning Beam Search and Chaining with Early Planning) substantially improve performance without retraining, it remains unresolved whether training can directly enhance planning reliability and extend the effective planning horizon.

References

Whether early commitment and horizon limitations manifest similarly in non-spatial reasoning modalities, and whether training can produce models that plan more reliably or over longer horizons, are important open questions.

— Video Models Reason Early: Exploiting Plan Commitment for Maze Solving  (2603.30043 - Newman et al., 31 Mar 2026) in Section 8 (Conclusion), final paragraph

This protocol ensures that the reported trajectory metrics are not confounded by differences in evaluated duration. The requirement of a predefined generation horizon remains a practical assumption of the current formulation. We do not use segment stitching in the reported experiments, and generation substantially beyond the training-length distribution has not been systematically evaluated.

— TKCAM: Text and Keyframe to Camera Trajectory Generation  (2610.11105 - Yang et al., 8 Oct 2026) in Appendix, Section Trajectory-Length Handling (Section 4.2)