Training language models to follow instructions with human feedback
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
THE RESEARCH ATLAS
MolmoAct: Action Reasoning Models that can Reason in Space
Depth-aware perception, spatial planning and low-level action prediction are organized into an explicit action-reasoning pipeline.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Study the intermediate spatial plan as an interface for inspecting and steering robot behavior.
Citation
@misc{lee2025molmoact,
title = {MolmoAct: Action Reasoning Models that can Reason in Space},
author = {Jason Lee and Jiafei Duan and Haoquan Fang and Yuquan Deng and Shuo Liu and Boyang Li and Bohan Fang and Jieyu Zhang and Yi Ru Wang and Sangho Lee and Winson Han and Wilbert Pumacay and Angelica Wu and Rose Hendrix and Karen Farley and Eli VanderBilt and Ali Farhadi and Dieter Fox and Ranjay Krishna},
year = {2025},
eprint = {2508.07917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.07917}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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