@inproceedings{milani2023towards,
author = {Milani, Stephanie and Kanervisto, Anssi and Ramanauskas, Karolis and Schulhoff, Sander and Houghton, Brandon and Mohanty, S. and Galbraith, Byron V. and Chen, Ke and Song, Yan and Zhou, Tianze and Yu, Bingquan and Liu, He and Guan, Kai and Hu, Yujing and Lv, Tangjie and Malato, Federico and Leopold, Florian and Raut, Amogh and Hautamaki, Ville and Melnik, Andrew and Ishida, Shu and Henriques, João F. and Klassert, Robert and Laurito, Walter and Novoseller, Ellen R. and Goecks, Vinicius G. and Waytowich, Nicholas R. and Watkins, David and Miller, J. and Shah, Rohin},
title = {Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition},
booktitle = {Machine Learning Research},
year = {2023},
month = {March},
abstract = {To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022. The BASALT challenge asks teams to compete to develop algorithms to solve tasks with hard-to-specify reward functions in Minecraft. Through this competition, we aimed to promote the development of algorithms that use human feedback as channels to learn the desired behavior. We describe the competition and provide an overview of the top solutions. We conclude by discussing the impact of the competition and future directions for improvement.},
url = {http://approjects.co.za/?big=en-us/research/publication/towards-solving-fuzzy-tasks-with-human-feedback-a-retrospective-of-the-minerl-basalt-2022-competition/},
pages = {2-18},
}