Two-level model helps long-horizon planning when data and compute are adequate
A higher-level planner sketches steps; a lower-level planner carries them out.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Gaoyue Zhou, Yvonne Wu, Zichen Jeff Cui, Nicolas Ballas, Mido Assran, Lerrel Pinto, Yann LeCun
- Universities and research institutions
- Courant Institute, New York University
Institution metadata: Original paper ↗
What they did and found
The study uses a two-level model: a higher part suggests future states; the lower part turns them into actions. In navigation and manipulation tasks, it performed better for long goals, but requires offline data and heavy compute.
Why it matters
This shows how to plan longer tasks without fixed rewards, but success depends on data quality and enough processing time.
High-level ideas map to doable future states, while the low level finds exact steps. This split keeps plans steadier as goals get farther.
More computing time can yield steadier progress toward distant goals, especially when data coverage is uneven across situations.
What remains uncertain
Performance depends on offline data coverage and the specific test tasks; inference may be slower because the planner searches many options.
Original sources · 1
- Hierarchical World Models with Implicit Dynamics ↗arXiv · 2026-10-30
Check the original paper for its authors, methods, version and access terms.
What is your take?
Ask a question, add useful context or share a different perspective. Keep the conversation respectful and grounded.