45 citations · 53 across the 16 of their papers we have counts for
5 papers · 1 filter
When Data Falls Short: Grokking Below the Critical Threshold
Vaibhav Singh, Eugene Belilovsky, Rahaf Aljundi
In this paper, we investigate the phenomenon of grokking, where models exhibit delayed generalization following overfitting on training data. We focus on data-scarce regimes where…
DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
Vaibhav Singh, Oleksiy Ostapenko, Pierre-André Noël +2
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) generation, yet their reliance on Transformer backbones limits inference efficiency…
End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards
AmirHossein Zamani, Tianhao Xie, Amir G. Aghdam +2
While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challen…
Incentivizing Permissionless Distributed Learning of LLMs
Joel Lidin, Amir Sarfi, Evangelos Pappas +3
We describe an incentive system for distributed deep learning of foundational models where peers are rewarded for contributions. The incentive system, \textit{Gauntlet}, has been d…
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training
Vaibhav Singh, Paul Janson, Paria Mehrbod +4
The ever-growing availability of unlabeled data presents both opportunities and challenges for training artificial intelligence systems. While self-supervised learning (SSL) has em…