Tuesday, 6 October 2026 · 8 min read
ALoDLM improves diffusion language models with token-adaptive recurrent refinement
ALoDLM introduces token-adaptive latent recurrence within diffusion language models, allowing tokens to commit at different depths based on prediction difficulty. Trained at 1.7B and 8B scales, it outperforms prior diffusion and autoregressive models across 11 benchmarks while maintaining fast parallel decoding.
Based on the paper ALoDLM: Adaptively Looped Diffusion Language Models by Liancheng Fang, Zhuowei Li, Youngeun Kim, Tianchen Zhao and 9 others
Challenges in diffusion language models and computation allocation
Autoregressive (AR) language models generate text token-by-token, which limits generation speed due to sequential decoding. Diffusion language models (DLMs) offer faster generation by predicting multiple tokens in parallel through iterative denoising of corrupted sequences.
However, DLMs lag behind AR models in generation quality. This is partly due to a computation–difficulty mismatch: within a partially observed sequence, some masked tokens are easy to predict, while others are harder and require more computation. Existing DLMs apply uniform computational depth to all masked tokens at each denoising step, wasting compute on easy tokens and insufficiently refining difficult ones.
Prior methods partially address this by deferring low-confidence tokens to later steps, but still apply fixed compute per step and discard intermediate latent states, limiting adaptive refinement. This motivates the need for token-adaptive computation allocation within each denoising step to better match token difficulty.
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