Scaling Beyond Masked Diffusion Language Models
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Recent research reveals that Masked diffusion models, while currently leading in perplexity scores, can be improved by 12% in FLOPs efficiency using a cross-entropy training objective. The study challenges the notion that perplexity is a reliable metric for comparing different diffusion models. Notably, uniform-state diffusion outperformed both autoregressive and Masked diffusion models on the GSM8K benchmark despite lower perplexity. Full details and resources are available at their project page.
New Insights Challenge Dominance of Masked Diffusion Language Models
Recent research reveals that Masked diffusion models achieve approximately 12% greater efficiency in floating-point operations (FLOPs) when trained with a cross-entropy objective. This study serves as the first comprehensive analysis of scaling laws for uniform-state and interpolating discrete diffusion methods.
When scaled to 1.7 billion parameters, uniform-state diffusion models outperformed both autoregressive and Masked diffusion models on the GSM8K benchmark, despite higher validation perplexity. This finding questions the assumption that Masked diffusion is the definitive future for diffusion language modeling.
The research suggests a reevaluation of metrics used to assess model efficacy, indicating that relying solely on perplexity may not fully capture a model's practical potential.
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📰 Original Source: https://arxiv.org/abs/2602.15014v1
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