Matthew R. Gormley

One might imagine that architectural variations within the dense transformer paradigm have a limited effect on accuracy. However, we demonstrate that this is not the case in the long context setting. Specifically, we show that a set of four minor architectural decisions --- all made by at least one of the Olmo, Llama, and Qwen dense model families --- have a compoundingly negative effect on long context extensibility. Any one of these choices alone has a minor impact on long context performance, but combining three or more can drop the performance downstream by up to 47%. Furthermore, these differences are not detectable from short-context loss or validation datasets. We show that much of the variation in long context ability across model families is driven by these architectural features and detectable from applying context extension early in pretraining. We demonstrate this with controlled ablations that hold data, tokenizer, and extension recipe fixed while varying normalization, GQA, pretraining context length, and sliding window attention. After over 170,000 GPU hours of training, we release the resulting set of models as OlmPool, a set of 26 comparable 7B models with checkpoints before and after long-context extension. This pool includes several architectures that outperform the Llama 3 architecture on long context extensibility. In an analysis of our ablation models, we identify patterns in attention sink behavior and attention distributions across context that are attributable to specific architectural differences.

Many important applications of long-context models require reasoning over large collections of items—summarizing trends across thousands of customer interactions, identifying patterns in research corpora, or analyzing behavioral data at scale. Evaluating whether models can analyze items atomically and aggregate findings requires information-dense tasks that make use of the full context, like writing a summary or describing trends across a collection. Yet these types of tasks often result in long-form outputs which are challenging to evaluate; as a result, current long-context benchmarks predominantly test retrieval-style tasks, where the correct answer depends on a small fraction of tokens and the rest is noise. We introduce OOLONG, a benchmark of distributional reasoning tasks that require analyzing individual chunks of text atomically and aggregating results. OOLONG includes synthetic tasks (OOLONG-synth) that enables controlled ablation of reasoning components, and a downstream setting (OOLONG-real) that requires reasoning over realistic conversational data. Tasks involve classification, counting, filtering by metadata, and temporal reasoning. Even frontier models struggle at this task at moderate context lengths, suggesting OOLONG captures capabilities not yet addressed by scale alone. We release the data and evaluation harness for OOLONG to enable further development of models that can reason over large quantities of text.