MiniMax海螺视频团队首次开源：Tokenizer也具备明确的Scaling Law

Core Viewpoint - The MiniMax Sea Cucumber video team has introduced a new scalable visual tokenizer pre-training framework (VTP) that addresses the limitations of traditional tokenizers in generating high-quality outputs from generative models, emphasizing the importance of understanding over mere pixel reconstruction [5][15][58]. Group 1: Traditional Tokenizer Limitations - Traditional tokenizers focus on pixel-level reconstruction, which does not necessarily translate to improved generation quality, leading to a saturation point where increased computational resources yield diminishing returns [4][15]. - The "pre-training scaling problem" indicates that better reconstruction accuracy can paradoxically lead to poorer generation performance, as traditional methods often overlook high-level semantic understanding [12][15]. Group 2: VTP's Approach and Innovations - VTP shifts the focus from pixel-level reconstruction to a more holistic understanding of visual semantics, integrating various representation learning methods to enhance the tokenizer's capabilities [26][30]. - The framework employs a multi-task loss function that combines understanding, reconstruction, and generation, allowing the tokenizer to produce semantically rich latent representations that improve downstream model performance [34][35]. Group 3: Empirical Findings and Performance Metrics - VTP demonstrates that injecting "understanding" into the tokenizer significantly enhances generation quality, with empirical evidence showing a positive correlation between understanding capabilities and generation performance [40][41]. - The VTP model achieved a zero-shot classification accuracy of 78.2% on ImageNet, surpassing the original CLIP's 75.5%, and exhibited superior reconstruction and generation capabilities compared to existing models [44]. Group 4: Scaling Law and Industry Implications - VTP reveals a scaling law for tokenizers, indicating that performance can improve with increased computational resources, data, and parameters, challenging the traditional view that scaling benefits only apply to main models [50][54]. - The findings suggest that investing in tokenizer development is crucial for enhancing overall generative system performance, positioning tokenizers as a core component worthy of long-term investment in the industry [58].