10 papers
Decomposer: Learning to Decompile Symbolic Music to Programs
Yewon Kim, Apurva Gandhi, David Chung +2
Musical performance involves executing a set of high-level musical instructions, yet recovering those instructions from the performance is a challenging inverse problem. We present…
PPT-Eval: A Benchmark for Computer-Use Agents on PowerPoint Tasks
Apurva Gandhi, Vishwas Suryanarayanan, Raja Hasnain Anwar +6
Creating and editing slides is a rich, multimodal activity that is ubiquitous in professional and educational settings, making it an ideal testbed for real-world computer-use agent…
Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time
Michael Y. Hu, Apurva Gandhi, Kyunghyun Cho +2
Data mixing decides how to combine different sources or types of data and is a consequential problem throughout language model training. In pretraining, data composition is a key d…
Recursive Agent Optimization
Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang +2
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new instantiations…
CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents
Lintang Sutawika, Aditya Bharat Soni, Bharath Sriraam R R +8
A prerequisite for coding agents to perform tasks on large repositories is code localization - the identification of relevant files, classes, and functions to work on. While reposi…
Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
Yueqi Song, Ketan Ramaneti, Zaid Sheikh +18
Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this wo…