新闻
Rethinking Neural Thickets Without Weight Perturbations
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers show that Neural Thickets gains come from output diversity and voting, not weight-space geometry. Frozen models with random logit perturbations match or exceed full weight perturbations.
- 为何重要
- Matters for engineers optimizing inference ensembles on language and vision tasks, especially under domain shift or reasoning workloads.
- 注意
- Results are empirical on specific tasks and validation sets. Generalization to other model architectures, scales, or domains remains unclear.
- language model
- reasoning
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