autoregressive model
PulseAugur coverage of autoregressive model — every cluster mentioning autoregressive model across labs, papers, and developer communities, ranked by signal.
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New 7B Uniform Diffusion Language Model 'Sumi' Released, Alongside Diffusion Model Advancements
Researchers have introduced Sumi, a 7-billion parameter uniform diffusion language model (UDLM) pretrained from scratch on 1.5 trillion tokens. This open-source model demonstrates competitive performance against autoreg…
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New Reflective Masking technique enables multi-turn reasoning in diffusion models
Researchers have introduced Reflective Masking (RM), a post-training technique that enables Mask Diffusion Models (MDMs) to perform multi-turn reasoning through iterative self-revision. Unlike autoregressive models that…
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New Parallel Jacobi Decoding speeds up image generation models
Researchers have developed a new method called Parallel Jacobi Decoding (PJD) to speed up autoregressive image generation models. This technique expands draft tokens in a two-dimensional spatial domain, allowing for par…
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New method enhances compositional generalization in autoregressive models
Researchers have developed a new method for composing autoregressive models, drawing inspiration from composition strategies used in diffusion models. This approach, based on a factorized-conditionals assumption, ensure…
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Neural networks tackle quantum Monte Carlo sign problem
Researchers have developed a novel method using neural autoregressive control variates to address the sign problem in quantum Monte Carlo simulations. This technique employs two autoregressive models, each confined to p…
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NVIDIA unveils Nemotron-Labs Diffusion language models for faster text generation
NVIDIA has introduced a new family of diffusion language models (DLMs) called Nemotron-Labs Diffusion, designed to overcome the limitations of traditional autoregressive models. These DLMs generate text by creating mult…
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New research tackles diffusion language model limitations
Researchers are exploring new methods to improve diffusion language models (DLMs), which offer faster inference than autoregressive models. Several recent papers introduce techniques to enhance DLM performance, includin…
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Generative models learn rules across two distinct training timescales
Researchers have identified two distinct timescales in generative model training: the point at which generations become rule-valid ($\tau_{\mathrm{rule}}$) and the point at which models begin reproducing training sample…