Autoregressive Token World Models › Summary

• A fully categorical world model can serialize observations, actions, and optional rewards, then predict world tokens with a causal next-token objective. Tokenization, target shifting, masks, and action semantics still require explicit design.
• Visual tokenizers impose finite representation budgets. VQ-VAE-style, residual, scalar, and lookup-free quantizers differ in geometry and training; no fixed choice guarantees task-relevant sufficiency or full code utilization.
• Actions can use discrete symbols, binned scalars, continuous heads, or latent interfaces. Latent transition codes require controllability evidence; action quantization error and sequential decoding cost have task-dependent control consequences.
• Frame-major layouts support simple online action interleaving. Position-major layouts remain valid offline factorizations but need care for online conditioning. Genie's masked next-frame decoder and Oasis's latent diffusion are distinct from raster categorical next-token decoding.
• An explicit token buffer retains a bounded record, while recurrent states and contextual caches can carry selected older information. Exact stored IDs do not guarantee useful retrieval, and per-step costs depend on the cache and attention implementation.
• Exposure bias concerns a history-distribution mismatch, not every rollout error. Constant conditional success probabilities yield $p^H$ flawless survival, but recovery can only improve current-step match, not restore survival. The miniature compares matched conditioning regimes, two training budgets, corrective fine-tuning against continued teacher forcing, and changed initial evidence against an exact oracle. It does not establish a universal repair or a controller benefit.

The next chapter studies diffusion denoising and flow-based transport as alternatives to categorical prediction. Continuous variants avoid discrete codebook rounding but can retain other compression and numerical losses. Both categorical and continuous models can represent multimodal futures. Total generation cost depends on the number and size of evaluations and available parallelism, not simply whether the output is discrete or continuous.