Summary of Discrete Distributions Are Learnable From Metastable Samples, by Abhijith Jayakumar et al.
Discrete distributions are learnable from metastable samplesby Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc…
Discrete distributions are learnable from metastable samplesby Abhijith Jayakumar, Andrey Y. Lokhov, Sidhant Misra, Marc…
Artificial Kuramoto Oscillatory Neuronsby Takeru Miyato, Sindy Löwe, Andreas Geiger, Max WellingFirst submitted to arxiv…
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglementby Hui Yuan, Yifan Zeng, Yue…
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Modelsby Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran,…
The Disparate Benefits of Deep Ensemblesby Kajetan Schweighofer, Adrian Arnaiz-Rodriguez, Sepp Hochreiter, Nuria OliverFirst submitted…
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMsby Tianyu Guo, Druv Pai, Yu Bai,…
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimizationby Chen Bo Calvin Zhang,…
A Unified View of Delta Parameter Editing in Post-Trained Large-Scale Modelsby Qiaoyu Tang, Le Yu,…
LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptationby Xuan Zhang, Fengzhuo…
Influence Functions for Scalable Data Attribution in Diffusion Modelsby Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae,…