Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights

Yulu Gan, Phillip Isola

Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights: 12 upvotes on Hugging Face Daily Papers, #17 of 48 papers on 2026-03-13. Day-by-day upvote history.

Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretraining as a distribution over parameter vectors, whose support already contains task-specific experts. We show that in small models such expert solutions occupy a negligible fraction of the volume of this distribution, making their discovery reliant on structured optimization methods such as gradient descent. In contrast, in large, well-pretrained models the density of task-experts increases dramatically, so that diverse, task-improving specialists populate a substantial fraction of the neighborhood around the pretrained weights. Motivated by this perspective, we explore a simple, fully parallel post-training method that samples N parameter perturbations at random, selects the top K, and ensembles predictions via majority vote. Despite its simplicity, this approach is competitive with standard post-training methods such as PPO, GRPO, and ES for contemporary large-scale models.

Paper page on Hugging Face · arXiv

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