DiffuSpace releases Clover-35B-A3B-Preview diffusion language model
The text-only model uses about 3B active parameters per token and a 128K context window.
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The text-only model uses about 3B active parameters per token and a 128K context window.
ALoDLM allocates diffusion-model computation by token difficulty and beats matching autoregressive baselines.
HC-DLM ties discrete tokens to a continuous latent and beats diffusion baselines on reasoning and LM1B.
HyperQ trains quantum residual branches in a frozen 1.1B diffusion model, beating the backbone by 4.71 points.
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