AgentOhana：为有效的Agent学习设计统一的数据和训练流水线

摘要

由大型语言模型（LLMs）驱动的自主代理引起了广泛的研究关注。然而，要充分利用LLMs在基于代理的任务中的潜力存在固有挑战，因为不同数据源的异质性特质包含了多轮轨迹。在本文中，我们介绍AgentOhana作为应对这些挑战的综合解决方案。AgentOhana汇总了来自不同环境的代理轨迹，涵盖了各种场景。它精心将这些轨迹标准化和统一为一致的格式，简化了用于代理训练的通用数据加载器的创建。利用数据统一化，我们的训练流程在不同数据源之间保持平衡，并在数据集分区和模型训练过程中保持设备之间的独立随机性。此外，我们提出了xLAM-v0.1，一个专为AI代理量身定制的大动作模型，展现出在各种基准测试中的出色性能。

English

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.

AgentOhana：为有效的Agent学习设计统一的数据和训练流水线

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

摘要

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