title: AgentOhana: Design Unified Data and Training Pipeline for Effective
    Agent Learning
summary: 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.
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paper_authors.name: Thai Hoang
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paper_authors.name: Yihao Feng
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paper_authors.name: Huan Wang
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paper_title: AgentOhana: Design Unified Data and Training Pipeline for Effective
    Agent Learning
paper_summary: 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.
paper_ai_keywords: agent-based tasks large language models (LLMs) AgentOhana agent trajectories data loader training pipeline equilibrium performance benchmarks Gen4{xLAM-v0.1 large action model