AI

ALoDLM: Adaptively Looped Diffusion Language Models

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Briefings

ALoDLM addresses the quality gap in diffusion language models by replacing uniform computation with token-adaptive latent recurrence. The model allocates computation based on token difficulty, using token-wise computation schedules as latent variables to learn prediction and allocation jointly. Trained at 1.7B and 8B parameter scales, ALoDLM outperforms all evaluated diffusion and autoregressive models across eleven benchmarks, achieving superior average benchmark scores while maintaining fast parallel decoding.

Key points

• ALoDLM introduces token-adaptive latent recurrence to allocate computation based on token difficulty rather than uniform depth.
• The model uses token-wise computation schedules as latent variables to learn joint token prediction and computation allocation.

Context

Diffusion language models enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive models. This study presents ALoDLM, which addresses this gap through token-adaptive latent recurrence.

Limitations

Evidence covers eleven benchmarks across two parameter scales. Results represent trained performance on optimized inference engines; no additional background or causal claims are supplied beyond the provided text.

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