Recent research highlights the potential of diffusion language models (DLMs). Owing to the parallel decoding design, they can generate thousands of tokens per second, resulting in exceptionally low latency for real-world applications [17][18][19]. Moreover, several recent DLMs have demonstrated performance on par with autoregressive (AR) models [8][9].

But is speed their only advantage? After rigorous investigations over the past few months, we discovered a more striking trait: diffusion models are super data learners under fixed data budgets. That is, given the same number of unique pre-training tokens, diffusion models consistently outperform AR counterparts of equal size—by trading additional FLOPs for improved learning. This reflects a roughly >3x data potential of AR models.

Such data potential is increasingly valuable as we approach the limits of available pre-training data [20], especially given that AR models show diminishing returns after just four epochs of data reuse [11]. Coincidentally, a concurrent study [1] explores similar topics. However, our careful analysis reveals several methodological issues in [1] that may lead to flawed conclusions.

In this post, we present preliminary results providing strong evidence for a clear “crossover” point where diffusion models outperform AR models. We then delve into the learning behavior of diffusion models to shed light on how this advantage emerges. Finally, we offer a detailed critique of the problematic methodologies in [1], aiming to guide more robust future research.

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