Measuring Progress in Deep Reinforcement Learning Sample Efficiency

9 February 2021

Papers citing "Measuring Progress in Deep Reinforcement Learning Sample Efficiency"

7 / 7 papers shown

Title
The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimation Reza Moravej Saurabh Bodhe Zhanguang Zhang Didier Chetelat Dimitrios Tsaras Yingxue Zhang Hui-Ling Zhen Jianye Hao M. Yuan 57 1 0 17 Feb 2025
A Survey of Reinforcement Learning for Optimization in Automation Ahmad Farooq Kamran Iqbal OffRL 89 1 0 13 Feb 2025
Will we run out of data? Limits of LLM scaling based on human-generated data Pablo Villalobos A. Ho J. Sevilla T. Besiroglu Lennart Heim Marius Hobbhahn ALM 33 109 0 26 Oct 2022
The Alignment Problem from a Deep Learning Perspective Richard Ngo Lawrence Chan Sören Mindermann 56 183 0 30 Aug 2022
Measuring the Algorithmic Efficiency of Neural Networks Danny Hernandez Tom B. Brown 235 94 0 08 May 2020
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 340 1,960 0 04 May 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 246 4,489 0 23 Jan 2020