2 citations · 4 across the 7 of their papers we have counts for
7 papers · 1 filter
Interleaved Head Attention
Sai Surya Duvvuri, Chanakya Ekbote, Rachit Bansal +6
Multi-Head Attention (MHA) is the core computational primitive underlying modern Large Language Models (LLMs). However, MHA suffers from a fundamental linear scaling limitation: $H…
Let's (not) just put things in Context: Test-Time Training for Long-Context LLMs
Rachit Bansal, Aston Zhang, Rishabh Tiwari +8
Progress on training and architecture strategies has enabled LLMs with millions of tokens in context length. However, empirical evidence suggests that such long-context LLMs can co…
The Art of Scaling Reinforcement Learning Compute for LLMs
Devvrit Khatri, Lovish Madaan, Rishabh Tiwari +6
Reinforcement learning (RL) has become central to training large language models (LLMs), yet the field lacks predictive scaling methodologies comparable to those established for pr…
Fast and Simplex: 2-Simplicial Attention in Triton
Aurko Roy, Timothy Chou, Sai Surya Duvvuri +5
Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size…
LASER: Attention with Exponential Transformation
Sai Surya Duvvuri, Inderjit S. Dhillon
Transformers have had tremendous impact for several sequence related tasks, largely due to their ability to retrieve from any part of the sequence via softmax based dot-product att…
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
Jui-Nan Yen, Si Si, Zhao Meng +5
Low-rank adaption (LoRA) is a widely used parameter-efficient finetuning method for LLM that reduces memory requirements. However, current LoRA optimizers lack transformation invar…
A Computationally Efficient Sparsified Online Newton Method
Fnu Devvrit, Sai Surya Duvvuri, Rohan Anil +3
Second-order methods hold significant promise for enhancing the convergence of deep neural network training; however, their large memory and computational demands have limited thei…