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language-modeling

topic3 events
papersTODAY 04:00 UTC

Discrete Beckmann Transport Models Target One-Step Language Generation

A new arXiv preprint introduces discrete Beckmann transport models, a framework for language modeling and reasoning that avoids the multi-step sampling usually required by discrete diffusion and flow approaches. The authors note that existing methods compress sampling steps only by distilling a pretrained autoregressive teacher, which limits the student to the teacher's performance. Their approach instead aims to generate text in a single step without relying on that distillation ceiling.

language-models/diffusion/research/inference
papersTODAY 04:00 UTC

Mimir paper proposes multilingual concept modeling beyond token-based LMs

A revised arXiv paper titled Mimir argues that current language modeling is organized around tokens, where corpora are split into tokens and models are trained on token-level objectives such as next-token prediction. The authors propose an alternative that works with concepts at a large multilingual scale. The submission is a replacement version of a cross-listed paper.

multilingual/research/language-models/arxiv
papersSEP 11 04:00 UTC

Study Argues Continuous Diffusion Can Scale Competitively for Language Modeling

A new arXiv paper revisits Plaid, a likelihood-based continuous diffusion model for text, to test the assumption that continuous diffusion scales worse than discrete alternatives. The authors report that with the right design, continuous diffusion can match discrete diffusion at scale. The work is a replacement version of a cross-listed machine learning preprint.

diffusion/language-models/scaling/research