Olmo

Researchers at New York University and the University of Texas at Austin analyzed output from 14 language models and identified six linguistic constructions that frontier models overuse compared with human writing. Smaller models such as Llama and the Olmo base model came closest to human text, and the authors point to post-training as the likely cause.

Ai2 released Olmo-core 3, an open training framework for large mixture-of-experts models. The company reports about 2.7× the throughput of its earlier FSDP-based implementation and benchmarks up to 1.2 trillion total parameters on 512 NVIDIA B300 GPUs. It is the infrastructure for Ai2's next MoE-based Olmo, not a model release.

Allen Institute for AI researchers conducted token-level analysis comparing their 7B-parameter Olmo 3 transformer and Olmo Hybrid models. The study finds hybrid architectures show a loss gap advantage of 0.04 on content words (nouns, verbs, adjectives) versus 0.02 on function words, while transformers match or exceed hybrids on repeated tokens and closing braces.