The May 24, 2025 UT Austin paper introduces the **Anchored Diffusion Language Model (ADLM)**, a novel approach that aims to improve discrete language modeling by addressing the limitations of traditional **Autoregressive (AR)** and standard **Diffusion Language Models (DLMs)**. AR models, such as GPT-3, generate text sequentially and struggle with complex reasoning, while existing DLMs, which use iterative masked-token prediction, lag behind AR models in quality. ADLM enhances DLMs by incorporating **anchor tokens**—semantically important words—that guide the denoising process through a two-component architecture: an **anchor network** and a **denoising network**. The training is formalized through the **Anchored Negative Evidence Lower Bound (ANELBO)** objective, which encourages the anchor network to predict these key tokens early, significantly reducing the **sample complexity** and improving both **likelihood modeling** (achieving better perplexity scores) and **generated text quality**. Furthermore, the concept of anchoring is successfully extended to AR models via **Anchored Chain-of-Thought (ACoT)** fine-tuning, demonstrating improved performance on **math and logical reasoning tasks**.Source:https://arxiv.org/pdf/2505.18456

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