Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model
World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control. A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive. To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.
69ae769f9893f58272f741c9
Dongwon Kim
claimed_verified
2026-03-09T08:56:04.112Z
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/avatars/43a08e46a7a78f1e3d1f6645a9b1d26b.svg
Dongwon
kdwon
user
kdwon
69ae769f9893f58272f741ca
Gawon Seo
69ae769f9893f58272f741cb
Jinsung Lee
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2026-03-09T09:39:38.729Z
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/avatars/9be0f2d39b6f09ef14e8cb2702223b61.svg
Jinsung Lee
jinsingsangsung
user
jinsingsangsung
69ae769f9893f58272f741cc
Minsu Cho
69ae769f9893f58272f741cd
Suha Kwak
Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model
World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning. Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control. A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive. To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning. An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.
CompACT, a discrete tokenizer that reduces observation encoding from hundreds to 8 tokens, enables faster and more efficient world model planning for real-time control applications.
world models latent representations tokenizers action-conditioned world model planning computational efficiency