Latent Dynamics · 2026-10-30
When More Planning Hurts: Learned Planners for World Models
Josef Chen
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
On PushT, a fixed supervised action updater (1,505 parameters) solves 8/16 reserved scenes. In the same reserved comparison, released LePlanner solves 16/16 with summed planning+encoding time 5.652s vs 59.079s for the updater. In a matched intervention, increasing early updates (4→8→12) yields 9/16, 8/16, 7/16 successes and extra updates do not rescue failures.
Why it matters
The authors show that, for a learned world-model planner, allocating more planning effort at a planning point can worsen eventual task success. They also report that their learned fixed updater does not meet a prespecified task–cost criterion and does not outperform a previously released learned controller on reserved scenes, despite having low parameter count.
Method
- Train a small supervised action updater (shared head uθ, 1,505 parameters) to imitate teacher improvement targets from optimization with a frozen latent-distance world-model predictor.
- Evaluate fixed planners (selected Adam, adapted iCEM, fixed learned8, and released LePlanner) on 16 reserved PushT scenes under shared controller conventions; score tasks and sum planning+encoding time.
- Run a matched continuation experiment: replay the first ten commands, then intervene on the number of solve updates (K∈{4,8,12}) for that solve while keeping later solves fixed to 8 updates.
Limitation
The authors state that for their reserved comparison “we compare full controllers under these shared conventions” but “we can’t draw conclusions about the controller architecture independently of population effects.” They also state they “do not test learned allocation policies,” and only one scene cluster shows outcome changes in the matched experiment.
Abstract (from arXiv)
On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection. The comparison planners perform as follows: Selected Adam (7 tasks solved, total planning and encoding time 218.440s), adapted iCEM (2 tasks solved, total planning and encoding time 49.309s), released LePlanner (16 tasks solved, total planning and encoding time 5.652s). The total planning and encoding time for our action updater is 59.079s. Note that our action updater does not satisfy the task-cost criterion we fixed before evaluation. We also conduct another matched experiment where we only replay the first ten commands in each context and then allow the next solve 4, 8 or 12 updates (subsequent solves all get 8 updates). Among all 16 contexts (8 scene clusters), the 3 budgets get 9, 8 and 7 successes, respectively. Interestingly, the extra updates did not rescue any failing task in this experiment. All the changes in success/failure happen in one scene cluster, where we observe that the extra updates decrease the costs of the selected latent world-model trajectories for our updater, while the tasks that used to be successes become failures. The code, weights, evaluators, execution traces and cost accounting accompany the paper.
Related papers
- When Low Prediction Error Misleads Planning: Diagnosing Representation, Dynamics, and Decision Failures in Latent World Models
- One-Step Prediction Fidelity Is a Poor Proxy for Planning Performance in JEPA World Models
- How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning
- How Long, Not How Close: A Learned Temporal Metric for Planning in Latent World Models
- Keeping JEPA World Models Plannable When Little of the Frame Moves
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First announced by arXiv: 2026-10-30 · Indexed by Pub-AI: 2026-10-30 UTC
When More Planning Hurts: Learned Planners for World Models
First announced by arXiv: 2026-10-30 · Original record ↗
- arxiv
- 2610.37891
Explicit source versions (1)
- v1 · Date not supplied
- Preprint available
Source date: 2026-10-30 · First detected: 2026-10-30 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · RSS guid=oai:arXiv.org:2610.37891v1; arxiv:announce_type=new; item pubDate (official metadata)
Abstract · arxiv
On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection. The comparison planners perform as follows: Selected Adam (7 tasks solved, total planning and encoding time 218.440s), adapted iCEM (2 tasks solved, total planning and encoding time 49.309s), released LePlanner (16 tasks solved, total planning and encoding time 5.652s). The total planning and encoding time for our action updater is 59.079s. Note that our action updater does not satisfy the task-cost criterion we fixed before evaluation. We also conduct another matched experiment where we only replay the first ten commands in each context and then allow the next solve 4, 8 or 12 updates (subsequent solves all get 8 updates). Among all 16 contexts (8 scene clusters), the 3 budgets get 9, 8 and 7 successes, respectively. Interestingly, the extra updates did not rescue any failing task in this experiment. All the changes in success/failure happen in one scene cluster, where we observe that the extra updates decrease the costs of the selected latent world-model trajectories for our updater, while the tasks that used to be successes become failures. The code, weights, evaluators, execution traces and cost accounting accompany the paper.