Sander Dieleman
sander.ai
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Overview
Sander Dieleman. A flurry of recent activity in the space of continuous diffusion models for language, after a few years of relative dormancy, suggests that this approach is making something of a comeback.
News
Continuous diffusion language models
Fully discrete methods dominated for a few years, but language models based on continuous diffusion are making a comeback.
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DiffusionGemma: 4x faster text generation
An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds.
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Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding Research
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain h
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Pushing the Limit of Efficient Inference-Time Scaling with Terminal Velocity Matching Luma
Terminal Velocity Matching (TVM) is a new single-stage training paradigm for efficient generation. While achieving the same sample quality, it exhibits 25x speedup compared to standard diffusion models when trained from scratch.
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