Rethinking Neural Thickets Without Weight Perturbations
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 already recovers much of the improvement, while keeping the model entirely frozen…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
