AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning

arXiv:2402.15506v1 Announce Type: cross
Abstract: 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 \textbf{AgentOhana} as a comprehensive solution to address these challenges. \textit{AgentOhana} aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent …

abstract agent agents arxiv attention autonomous autonomous agents challenges cs.ai cs.cl cs.lg data data sources design diverse language language models large language large language models llms nature paper pipeline research tasks training training pipeline type unified data