The authors built ALoDLM around token-adaptive latent recurrence.
Adaptive token computation narrows diffusion language models’ quality gap
ALoDLM recurrently refines difficult token representations while committing easier predictions early, improving benchmark scores without giving up parallel decoding.
Big Tech
Liancheng Fang · Zhuowei Li · Youngeun Kim · Tianchen Zhao · Rajat Koner · Jiaye Wu · +7 more
University of Illinois Chicago · Amazon AGI · Korea University
Research Digest··2 min read
Fang et al.
Why this paper
From Amazon AGI and 2 others
In one line
Diffusion language models can match autoregressive quality by allocating more recurrent compute to hard tokens while easy tokens commit early.
What we could check
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- ✓Reports numbers on named benchmarks
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What researchers are saying
Amazon AGI presents Adaptively Looped Diffusion Language Models Outperforms all evaluated DLMs and the corresponding AR baselines in average benchmark score proj: alo-dlm.github.io abs: arxiv.org/abs/2610.04198 repo: github.com/amazon-science… model: huggingface.co/amazon/ALoDLM-…
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