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The Neural Thickets hypothesis posits that pretrained language models lie in dense neighborhoods of task-specific experts in weight space, which random Gaussian perturbations can uncover. We reproduce the reported gains, but show that they do not require this geometric explanation. Perturbing a single decoder block alr...
Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and reach higher sample quality. Standard distillation aligns the latent at each position to a co-located teacher feature, tying the latent layout to the teacher's. We show...
A two-stage distillation pipeline first trains the causal student to match the teacher’s trajectories under a chunk-wise causal attention mask, then applies Distribution Matching Distillation (DMD) with backward simulation to close the few-step quality gap.
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