AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens

• URL: http://arxiv.org/abs/2606.07185v1
• Date: Fri, 05 Jun 2026 11:49:28 GMT
• Title: AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens
• Abstract summary: We propose AdaTok, a self-budgeting discrete 1D tokenizer.<n>AdaTok combines Prioritized Representation Learning, which orders tokens with nested tail masking, with Adaptive Token Allocation.<n>On ImageNet-1K, AdaTok-Full reaches rFID 1.31 at 256 tokens, while AdaTok-Adaptive attains rFID 1.50 using only 118 tokens on average, outperforming discrete 1D baselines at comparable budgets.
• Score: 39.0104982235623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a uniform budget overspends on simple inputs and underserves complex ones. Existing elastic tokenizers expose variable-length reconstructions, but often leave token length as a deployment-time operating point, a search target, or an external prediction rather than an output of the tokenizer itself. In this work, we ask whether a discrete visual tokenizer can budget itself in one pass. Our central finding is that actionable elasticity requires a representation--allocation co-design: prefixes must remain decodable across budgets, and the tokenizer must learn which prefix each image needs. We propose AdaTok, a self-budgeting discrete 1D tokenizer. AdaTok combines Prioritized Representation Learning, which orders tokens with nested tail masking and resolves budget-dependent semantic shift through Multi-Head LoRA decoder heads, with Adaptive Token Allocation, which trains a lightweight deterministic-group GRPO policy over candidate budgets. Dynamic Pareto Weighting balances fidelity and efficiency during policy training without manual trade-off sweeps. On ImageNet-1K, AdaTok-Full reaches rFID 1.31 at 256 tokens, while AdaTok-Adaptive attains rFID 1.50 using only ~118 tokens on average, outperforming discrete 1D baselines at comparable budgets. In autoregressive image generation, the shorter adaptive representation yields ~2.1x throughput over a fixed 256-token decode, suggesting that visual token count can be learned as a content-conditioned output rather than set as a fixed hyperparameter.

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