Venue: ICLR 2026 · Authors: Junha Chun, Youngjoon Jeong, Taesup Kim · arXiv: 2506.01392 · Category: World model for actions · Trend tag: efficient world-model planning.

flowchart LR
  Img[224x224 RGB obs] --> Enc[DINO-ViT-S/16<br/>14x14 = 196 tokens]
  Enc --> Drop[Token dropout<br/>keep 1-p fraction]
  Drop --> WM[Transformer world model<br/>randomized grouped attention]
  WM --> Roll[Sparse latent rollout<br/>imagination]
  Roll --> Plan[Planning / trajectory<br/>optimization]
  Plan --> Act[Action]

World-model-based planning lets agents simulate futures, but latent rollout over many visual tokens is expensive — a severe constraint in resource-limited robotics where planning happens online.

A sparsely trained transformer visual world model that reduces the number of tokens processed during forward prediction:

• Randomized grouped attention during training so the model can flexibly adjust the token count at inference based on available compute (process only (1−p)N of N tokens).
• Sparse imagination: drop a fraction p of patch tokens during latent rollout, accelerating planning while preserving control fidelity.
• Encoder is DINO-ViT-S/16 on 224×224 images, yielding 14×14 = 196 spatial tokens. Applicable from test-time trajectory optimization to tasks driven by recent VLAs.

• Planning-time reductions at 50% dropout (per Table 2): PushT 173s→82s (52.6%), Pointmaze 184s→93s (49.5%), Wall 79s→53s (32.9%), Block Pushing 297s→208s (30.0%).
• Task success preserved or improved at moderate dropout (10–50%): Wall 85.0%→95.0%, Rope 63.3%→73.3%, Pointmaze 98.3%→100.0%, PushT 75.0%→70.0%.
• Evaluated across six environments: Pointmaze, Wall, PushT, Granular, Rope, Block Pushing.

Shows that dropping visual tokens during imagination can nearly halve planning latency with little or no loss in task performance, making world-model planning more practical for compute-constrained robots. A targeted efficiency contribution within the "world model as actionable substrate" agenda.

• arXiv: https://arxiv.org/abs/2506.01392
• OpenReview: https://openreview.net/forum?id=faxcxKINBC

• ICLR 2026 Survey
• VLA Architectures review (world-model category)

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