2 citations · 3 across the 7 of their papers we have counts for
7 papers
Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models
Xiaoze Liu, Dhananjay Ram, Yuting Zhang +3
We introduce Mutual Reinforcement Learning, a framework for concurrent RL post-training in which heterogeneous LLM policies exchange typed experience while keeping separate paramet…
Reinforcement-aware Knowledge Distillation for LLM Reasoning
Zhaoyang Zhang, Shuli Jiang, Yantao Shen +6
Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such model…
Learning to Focus: Focal Attention for Selective and Scalable Transformers
Dhananjay Ram, Wei Xia, Stefano Soatto
Attention is a core component of transformer architecture, whether encoder-only, decoder-only, or encoder-decoder model. However, the standard softmax attention often produces nois…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
DEM: Distribution Edited Model for Training with Mixed Data Distributions
Dhananjay Ram, Aditya Rawal, Momchil Hardalov +2
Training with mixed data distributions is a common and important part of creating multi-task and instruction-following models. The diversity of the data distributions and cost of j…
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
Haozheng Fan, Hao Zhou, Guangtai Huang +6
Getting large language models (LLMs) to perform well on the downstream tasks requires pre-training over trillions of tokens. This typically demands a large number of powerful compu…
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
Qingru Zhang, Dhananjay Ram, Cole Hawkins +2
Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture lo…