Summary of Agentohana: Design Unified Data and Training Pipeline For Effective Agent Learning, by Jianguo Zhang et al.
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
by Jianguo Zhang, Tian Lan, Rithesh Murthy, Zhiwei Liu, Weiran Yao, Ming Zhu, Juntao Tan, Thai Hoang, Zuxin Liu, Liangwei Yang, Yihao Feng, Shirley Kokane, Tulika Awalgaonkar, Juan Carlos Niebles, Silvio Savarese, Shelby Heinecke, Huan Wang, Caiming Xiong
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces AgentOhana, a comprehensive solution for agent-based tasks powered by large language models (LLMs). The heterogeneous nature of diverse data sources featuring multi-turn trajectories presents challenges in fully harnessing the potential of LLMs. AgentOhana aggregates and standardizes agent trajectories from distinct environments into a consistent format, enabling the creation of a generic data loader optimized for agent training. Our training pipeline maintains equilibrium across different data sources and preserves independent randomness 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine robots or artificial intelligence (AI) that can work together to accomplish tasks. This paper helps make this happen by creating a system called AgentOhana. The problem is that different sources of data have different formats and structures, making it hard for AI agents to learn from them. AgentOhana solves this by taking in all these different data sources, standardizing them into one format, and then training AI models on them. This leads to better performance and more efficient learning. We also introduce a new AI model called xLAM-v0.1 that does very well on various tests. |
