Pranjal Aggarwal

Gym-Anything for Robotics: Scaling physical simulations with digital agents

Pranjal Aggarwal, Sean Welleck

Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents

Pranjal Aggarwal, Sean Welleck

cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents

Pranjal Aggarwal, Lawrence Keunho Jang, Sean Welleck, 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.