7 citations · 13 across the 11 of their papers we have counts for
4 papers · 1 filter
SDS -- See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration
Maria Stamatopoulou, Jeffrey Li, Dimitrios Kanoulas
Imagine a robot learning locomotion skills from any single video, without labels or reward engineering. We introduce SDS ("See it. Do it. Sorted."), an automated pipeline for skill…
Better Alignment with Instruction Back-and-Forth Translation
Thao Nguyen, Jeffrey Li, Sewoong Oh +4
We propose a new method, instruction back-and-forth translation, to construct high-quality synthetic data grounded in world knowledge for aligning large language models (LLMs). Giv…
DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis +56
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardize…
Language models scale reliably with over-training and on downstream tasks
Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar +22
Scaling laws are useful guides for derisking expensive training runs, as they predict performance of large models using cheaper, small-scale experiments. However, there remain gaps…