14 citations · 22 across the 9 of their papers we have counts for
4 papers · 1 filter
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Yapei Chang, Yekyung Kim, Michael Krumdick +4
Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM bac…
BEARCUBS: A benchmark for computer-using web agents
Yixiao Song, Katherine Thai, Chau Minh Pham +3
Modern web agents possess computer use abilities that allow them to interact with webpages by sending commands to a virtual keyboard and mouse. While such agents have considerable…
CLIPPER: Compression enables long-context synthetic data generation
Chau Minh Pham, Yapei Chang, Mohit Iyyer
LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a co…