Search Results for author:
Found 2 papers, 0 papers with code
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
We consider a general family of heavy-tail gradients that follow a power-law distribution, we aim to minimize the error resulting from quantization, thereby determining optimal values for two critical parameters: the truncation threshold and the quantization density.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
