4 papers · 1 filter
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…
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…
Go-Browse: Training Web Agents with Structured Exploration
Apurva Gandhi, Graham Neubig
One of the fundamental problems in digital agents is their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncer…
Inducing Programmatic Skills for Agentic Tasks
Zora Zhiruo Wang, Apurva Gandhi, Graham Neubig +1
To succeed in common digital tasks such as web navigation, agents must carry out a variety of specialized tasks such as searching for products or planning a travel route. To tackle…