Graham Neubig

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Sohyeon Kim, Yoonho Lee, Bo Liu, et al.

Efficient Test-Time Adaptation through Human-AI Interaction

Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao, et al.

Recursive Agent Optimization

Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang, et al.

What do Language Models Learn and When? The Implicit Curriculum Hypothesis

Emmy Liu, Kaiser Sun, Millicent Li, et al.

Gym-Anything: Turn any Software into an Agent Environment

Pranjal Aggarwal, Graham Neubig, Sean Welleck

Reasoning over mathematical objects: on-policy reward modeling and test time aggregation

Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim, et al.

This paper tackles how AI language models can better solve complex math and science problems that require deriving formally structured mathematical expressions (like equations or proofs) rather than just picking a number or multiple choice answer. The researchers created a new training dataset called Principia, developed improved training methods using AI judges that learn during training, and showed how to use extra computation at test time to aggregate multiple solution attempts for better results.