DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models | NSF Public Access Repository

skip to main content

An official website of the United States government Here's how you know

Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Citation Details

This content will become publicly available on May 1, 2026

DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models

Award ID(s):: 2415226

PAR ID:: 10633727

Author(s) / Creator(s):: Song, R; Liu, C; Wu, C; Li, A; Liu, D; Wu, Y N; Geng, T

Publisher / Repository:: International Conference on Learning Representations (ICLR)

Date Published:: 2025-05-01

ISSN:: 0000-0000

Format(s):: Medium: X

Sponsoring Org:: National Science Foundation

Free Publicly Accessible Full Text
This content will become publicly available on May 1, 2026
Conference Proceeding:
The DOI is not currently available.