AI RESEARCH
Autoregressive vs. Masked Diffusion Language Models: A Controlled Comparison
arXiv CS.CL
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ArXi:2603.22075v1 Announce Type: new We present a controlled empirical comparison between autoregressive (AR) and masked diffusion (MDLM) language models. Both models are trained on identical data (50M tokens from TinyStories), identical compute budget (20,000 steps, batch size 32, sequence length 512), and identical hardware (NVIDIA H100 80GB), isolating the generation paradigm as the sole variable. We report three findings. First, both paradigms achieve comparable.