Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model

• URL: http://arxiv.org/abs/2506.05289v2
• Date: Fri, 10 Oct 2025 16:20:43 GMT
• Title: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model
• Authors: Pingyu Wu, Kai Zhu, Yu Liu, Longxiang Tang, Jian Yang, Yansong Peng, Wei Zhai, Yang Cao, Zheng-Jun Zha,
• Abstract summary: AliTok is a novel Aligned Tokenizer that alters the dependency structure of the token sequence.<n>Building upon AliTok, a standard decoder-only autoregressive model with just 177M parameters achieves a gFID of 1.44 and an IS of 319.5 on the ImageNet-256 benchmark.
• Score: 69.79418000132995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autoregressive image generation aims to predict the next token based on previous ones. However, this process is challenged by the bidirectional dependencies inherent in conventional image tokenizations, which creates a fundamental misalignment with the unidirectional nature of autoregressive models. To resolve this, we introduce AliTok, a novel Aligned Tokenizer that alters the dependency structure of the token sequence. AliTok employs a bidirectional encoder constrained by a causal decoder, a design that compels the encoder to produce a token sequence with both semantic richness and forward-dependency. Furthermore, by incorporating prefix tokens and employing a two-stage tokenizer training process to enhance reconstruction performance, AliTok achieves high fidelity and predictability simultaneously. Building upon AliTok, a standard decoder-only autoregressive model with just 177M parameters achieves a gFID of 1.44 and an IS of 319.5 on the ImageNet-256 benchmark. Scaling up to 662M parameters, our model reaches a gFID of 1.28, surpassing the state-of-the-art diffusion method while achieving a 10x faster sampling speed. The code and weights are available at https://github.com/ali-vilab/alitok.

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