TiDAR: The Breakthrough Language Model Architecture Merging Diffusion and Autoregression

TiDAR: The Next-Gen Language Model Architecture Merging Diffusion and Autoregression This article answers the core question: How can language models maintain generation quality while drastically improving efficiency, achieving a balance between high throughput and optimal GPU utilization? Introduction: The Efficiency-Quality Dilemma in Language Models Core question of this section: What inherent trade-offs exist between generation efficiency and quality in current mainstream language models? As artificial intelligence evolves toward general intelligence, the success of large language models (LLMs) relies heavily on leveraging GPU computational resources effectively. However, the two dominant language model architectures—autoregressive (AR) models and diffusion language models (dLMs)—face an …