How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning

Changbai Li; Sirui Li; Yichen Yang; Tongfei Chen; Zichao Feng; Shuwei Shao; Huobin Tan

cs.ROOct 6, 2026

How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning

Authors: Changbai Li, Sirui Li, Yichen Yang, Tongfei Chen, Zichao Feng, Shuwei Shao, Huobin Tan

Organizations: Beihang University · Chengdu University of Technology · Nanyang Technological University

Abstract

Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservatively large planning budgets. We show that competitive task performance can be achieved without agreement with the Full-budget action, that sufficient budgets vary across model--task pairs, and that iterative planners repeatedly encode solve-invariant context. To address these inefficiencies, we propose {SufficientPlan}, a simple deployment framework that requires no modification to pretrained world models or planner updates. Its {Paired Sequential Budget Certification (PSBC)} component uses paired closed-loop evidence to search for and certify a reduced model--task-specific budget within a predefined Full-performance tolerance. Its {Static-Context Reuse (SCR)} component caches observation and goal representations across search iterations while preserving candidate-dependent planning and selected actions. Experiments across multiple world-model backbones and visual-control tasks show that SufficientPlan substantially reduces search budgets and planning latency while maintaining competitive control performance.

Figures & tables

Figure 1: Task success without Full-budget action agreement. On INTACT Push-T, the calibrated and Full planners follow different task- and action-space trajectories but both achieve 96% success, using 2,400 and 9,000 scored sequences per solve, respectively.

Figure 2: Paired Sequential Budget Certification (PSBC). PSBC compares the current budget with the full-budget reference on paired calibration episodes. Uncertain cases receive additional paired evidence, insufficient budgets are increased, the first proposal satisfying the certification criterion is returned as

B^{\star}

and frozen for held-out deployment.

Figure 3: Static-Context Reuse (SCR). SCR encodes the solve-invariant observation history and goal once and reuses them across

K

CEM iterations, reducing cost-stage static-encoder calls from

2K

2

without changing candidate-dependent planning or CEM updates.

Figure 4: Matched-budget evaluation of Static-Context Reuse (SCR). (a) Matched-budget success rates and episode-level outcome consistency. (b) Mean planning-latency reduction. (c) Planning speedup across backbone–task settings. SCR preserves all evaluated outcomes while achieving up to

2.18\times

speedup.

Planner	Deployment	Push-T		Cube		Reacher		TwoRoom
Planner	Deployment	Budget	Success	Budget	Success	Budget	Success	Budget	Success
LeWM	Full CEM	Full	85	Full	71	Full	82	Full	91
LeWM+PSBC	Calibrated CEM	50.0%	88	13.3%	74	80.0%	82	30.0%	91
Fast-LeWM	Full CEM	Full	95	Full	68	Full	82	Full	94
Fast-LeWM+PSBC	Calibrated CEM	26.7%	89	13.3%	73	80.0%	81	50.0%	94
PRISM	Official PoG-MPPI	Full	88	Full	78	–	–	–	–

Table 1: Comparison of full-budget planning and PSBC-calibrated planning across different world-model planners and control tasks. Budget denotes the planning budget relative to the corresponding full planner, and Success denotes the task success rate (%). “Full” indicates the original full planning budget, while “–” denotes unavailable results.

Figure 5: SCR scaling with planning depth on Fast-LeWM/Push-T. (a) Mean planning latency of the uncached and SCR-enabled planners. (b) Measured speedup and analytical reduction in solve-static encoder calls. Repeated measurements show no reliable speedup at one iteration, whereas SCR reaches

2.45\times

speedup at 30 iterations as repeated static encoding becomes more prominent.

Figure 6: Paired qualitative comparison on Cube. From the same initial state and goal, LeWM+PSBC succeeds while Full LeWM fails; Ground Truth is shown as reference, and columns progress from left to right.

Backbone	Configuration	PSBC	SCR	Scored Seq./Solve	Budget (% Full)	SR (%)	$\bm{\Delta}$ SR (pp)
INTACT	Full Actor-CEM	✗	✗	9,000	100.0	96	0
	+ PSBC	✓	✗	2,400	26.7	96	0
	+ PSBC + SCR	✓	✓	2,400	26.7	96	0
Fast-LeWM	Full CEM	✗	✗	9,000	100.0	90	0
	+ PSBC	✓	✗	2,400	26.7	89	$-1$
	+ PSBC + SCR	✓	✓	2,400	26.7	89	$-1$

Table 2: Incremental ablation on the frozen Push-T development set. PSBC reduces the deployed search budget while retaining near-Full success, and SCR preserves the resulting budget and control outcome.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A: Global fixed budgets versus PSBC-selected budgets. (a) Numbers of model–task pairs for which each global budget is below, equal to, or above the PSBC-selected budget. (b) Selected budget fractions across the 16 evaluated pairs.

Quantity	Value
Full planning budget	9,000
PSBC deployment budget	600
Full time per episode	34.28 s
PSBC time per episode	10.93 s
Time saving per episode	23.35 s

Appendix

Table A: Deployment efficiency on PRISM/Cube. PSBC reduces per-episode planning time relative to Full planning.

Figure B: Success–budget profiles across world-model planners and tasks. Lines show development-set success rates across planning budgets, while diamonds and stars denote held-out Full and PSBC results, respectively. Development curves and held-out markers correspond to distinct evaluation splits.

Task	Seed	$\bm{B^{\star}}$	Stop $n$	$\bm{q_{B,n}}$	$\bm{q_{B,100}}$	SR: $B$ /Full
Push-T	0	3,300	20	0.956	0.510	88/90
Push-T	1	1,800	20	0.956	0.799	88/87
Cube	0	900	80	0.972	0.971	88/82
Cube	1	600	100	0.981	0.981	92/85

Appendix

Table B: Posterior evolution after PSBC certification on PRISM.

q_{100}

retrospectively evaluates the locked 100-episode set and is not used in the original stopping decision.

Budget	% Full	SR	Full SR	$\bm{\Delta}$ SR	Rescued	Broken	$\bm{q_{B,100}}$	Empirical Accept	Fixed- $n$ Cert.
300	3.3	83	85	$-2$	10	12	0.503	✓	✗
600	6.7	92	85	$+7$	13	6	0.981	✓	✓
1,200	13.3	89	85	$+4$	12	8	0.911	✓	✗
2,400	26.7	91	85	$+6$	10	4	0.984	✓	✓

Appendix

Table C: Budget-selection criteria on PRISM/Cube. All candidates use the same 100 paired development episodes with a 2-point tolerance; fixed-

n

certification requires

q_{B,100}\geq 0.95

Figure C: Task success and action refinement on INTACT Push-T. (a) Development success rate across 100 episodes at different CEM iterations. (b) Mean RMS distance between the first action block at each iteration and its 30-iteration counterpart, with paired bootstrap 95% confidence intervals. Eight and 30 iterations achieve the same 96% success rate despite selecting different actions.

Deployment	Actor Prior	PSBC	SCR	Seq./Solve	Budget (% Full)	Resulting SR (%)	$\bm{\Delta}$ SR vs Direct
Direct	✓	✗	✗	0	0.0	83	0
Guarded-A	✓	✗	✗	384	4.3	88	$+5$
Pure CEM	✗	✗	✗	9,000	100.0	92	$+9$
Full Actor-CEM	✓	✗	✗	9,000	100.0	96	$+13$
Actor-CEM + PSBC	✓	✓	✗	2,400	26.7	96	$+13$
SufficientPlan	✓	✓	✓	2,400	26.7	96	$+13$

Appendix

Table D: INTACT deployment ablation on the frozen Push-T development set. We isolate the effects of actor-guided initialization, PSBC, and SCR.

Figure D: Sequential certification trace of PSBC on PRISM/Cube. Curves show the posterior probability that each candidate budget is non-inferior to Full planning within the prescribed tolerance as paired calibration episodes accumulate. At 100 episodes, the 600-sequence proposal crosses the 0.95 certification threshold and is returned as the certified operating budget.

Figure E: Multi-seed success rates on Push-T and Cube. Bars and error bars report the mean and standard deviation over evaluation seeds $\{0,1,42\}$ , and markers show the individual results. Percentages above the PSBC bars denote the corresponding deployment budgets relative to Full planning.

Figure F: Paired qualitative results on Reacher, TwoRoom, and Push-T. From identical initial states and goals, LeWM+PSBC succeeds while Full LeWM fails; rows correspond to the three tasks and columns progress from left to right.

$\bm{\delta}$ (pp)	0.90	0.95	0.99
	$\bm{\tau}$
0	100	–	–
2	100	100	–
5	80	80	100

Appendix

Table E: Sensitivity of the 600-sequence proposal on PRISM/Cube. Entries report the paired-episode inspection at which the proposal is first certified. “–” indicates that the proposal is not certified within 100 paired episodes.

Budget	Iter.	Uncached (s)	SCR (s)	Static Enc. Reduction	Latency Reduction	Speedup
300	1	0.4521	0.4571	0.0%	$-1.1$ %	0.99 $\times$
600	2	1.2422	0.8000	50.0%	35.6%	1.55 $\times$
1,200	4	2.0664	1.0660	75.0%	48.4%	1.94 $\times$
2,400	8	3.4617	1.7080	87.5%	50.7%	2.03 $\times$
4,500	15	6.2457	2.6330	93.3%	57.8%	2.37 $\times$
9,000	30	11.4736	4.6796	96.7%	59.2%	2.45 $\times$

Appendix

Table F: SCR scaling on Fast-LeWM/Push-T. SCR provides no reliable speedup at one iteration, while its latency benefit increases with search depth.

Contract	INTACT	Fast-LeWM
Candidate population/hash equal	✓	✓
Candidate cost equal	✓	✓
CEM mean/std updates equal	✓	✓
Final selected action equal	✓	✓
Solver RNG state equal	✓	✓
Model buffers unchanged	✓	✓

Appendix

Table G: Full-budget numerical equivalence contract for SCR. Uncached and SCR-enabled planners are evaluated with common random numbers at the maximum 9,000-sequence budget. The contract verifies the internal planning trajectory rather than only aggregate task success.

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Open paper AskPDF arXiv

Planning with a learned latent world model is a promising route to control from raw pixels, but a strong world model alone is not enough. We show this experimentally: even with a perfect world model (operationalized by replacing the learned forward predictor with an idealized rollout of the true environment dynamics), a finite-budget sample-based planner still fails on some tasks, indicating that the bottleneck can lie in search rather than in world-model accuracy. Motivated by this gap, we propose IMWM (Intuition Model + World Model), which pairs the world model with an intuition model trained from demonstrations to recognize promising actions. The two models collaborate through three lightweight components: (i) Retrieval Initialization, which initializes the planner's action proposal from a retrieved demonstration; (ii) Hybrid Cost, which combines the intuition score with the world-model rollout cost; and (iii) a Reliability Gate, which adjusts how much the planner trusts intuition in each setting. Across four pixel-based goal-reaching tasks (Two-Room, Reacher, Push-T, and OGBench-Cube), IMWM has higher mean success than the world-model-only planner on all four, with the largest gains on Two-Room (99.2%, +11.5 percentage points) and OGBench-Cube (94.7%, +28.5 percentage points).

Abstract

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