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

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. 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. Large language models (LLMs) have shown strong abilities in code generation, mathematical reasoning, conversational AI, and AI agents (OpenAI, 2023; Jiang et al., 2023; Zhang et al., 2023; Liu et al., 2023a; Nijkamp et al., 2023). Among these, LLM-powered autonomous agents are gaining increasing attention.