AgentOhana: Design Unified Data and Training Pipeline for Effective
  Agent Learning
Authors: name: Jianguo Zhang, name: Tian Lan, name: Rithesh Murthy, name: Zhiwei Liu, name: Weiran Yao, name: Juntao Tan, name: Thai Hoang, name: Liangwei Yang, name: Yihao Feng, name: Zuxin Liu, name: Tulika Awalgaonkar, name: Juan Carlos Niebles, name: Silvio Savarese, name: Shelby Heinecke, name: Huan Wang, name: Caiming Xiong

Autonomous agents powered by large language models (LLMs) have garnered
significant research attention. However, fully harnessing the potential of LLMs
for agent-based tasks presents inherent challenges due to the heterogeneous
nature of diverse data sources featuring multi-turn trajectories. In this
paper, we introduce AgentOhana as a comprehensive solution to address
these challenges. AgentOhana aggregates agent trajectories from
distinct environments, spanning a wide array of scenarios. It meticulously
standardizes and unifies these trajectories into a consistent format,
streamlining the creation of a generic data loader optimized for agent
training. Leveraging the data unification, our training pipeline maintains
equilibrium across different data sources and preserves independent randomness
across devices during dataset partitioning and model training. Additionally, we
present xLAM-v0.1, a large action model tailored for AI agents, which
demonstrates exceptional performance across various benchmarks.