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Found 7 papers, 5 papers with code
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method for enhancing performance.
Compression Represents Intelligence Linearly
We open-source our compression datasets as well as our data collection pipelines to facilitate future researchers to assess compression properly.
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
In this work, we explore a "pre-train, and search" paradigm for efficient sharding.
Elastic Deep Learning in Multi-Tenant GPU Clusters
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism
A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs).
