Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension
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…
Olmo Hybrid: From Theory to Practice and Back
Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention. Yet there is no…
Rethinking the Evaluation of Harness Evolution for Agents
We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance…
Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through complex reasoning. While automated scientific…
DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are trained on easily verifiable short-form QA tasks via…
Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizations and are sensitive to modeling choices.…
Why Are Linear RNNs More Parallelizable?
The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. While prior work establishes the expressivity benefits of…
Efficiently Representing Algorithms With Chain-of-Thought Transformers
The increasing popularity of \emph{reasoning} models -- language models that output a series of reasoning or thought tokens before producing an answer -- is justified, in part, by theoretical…
Meta-Reinforcement Learning with Self-Reflection for Agentic Search
This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent…
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks. These tasks span…