Summary of Agentbank: Towards Generalized Llm Agents Via Fine-tuning on 50000+ Interaction Trajectories, by Yifan Song et al.
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories
by Yifan Song, Weimin Xiong, Xiutian Zhao, Dawei Zhu, Wenhao Wu, Ke Wang, Cheng Li, Wei Peng, Sujian Li
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach for surfacing generalized agent capabilities in large language models (LLMs) is proposed, focusing on fine-tuning agent-environment interaction trajectories. The AgentBank dataset, comprising over 50k diverse high-quality interaction trajectories across 16 tasks and five distinct agent skill dimensions, is introduced. A scalable annotation pipeline enables the generation of a trajectory dataset with minimized difficulty bias. LLMs are fine-tuned on AgentBank to produce a series of agent models, Samoyed. Comparative experiments demonstrate the effectiveness of scaling interaction trajectory data for acquiring generalized agent capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can get better at certain tasks by learning from how they interact with different environments. This paper creates a big dataset called AgentBank that contains many examples of these interactions. They use this data to train new models, called Samoyed, and show that these models are more generally useful than before. |
Keywords
» Artificial intelligence » Fine tuning
