GOAT: A Training Framework for Goal-Oriented Agent with Tools
📰 ArXiv cs.AI
Learn how to fine-tune LLM agents for goal-oriented tool use with the GOAT framework, improving their ability to reason and interact with complex tools
Action Steps
- Apply the GOAT framework to fine-tune LLM agents without human annotation
- Use API documents to automatically synthesize goal-oriented API execution data
- Configure the GOAT framework to optimize LLM agent performance
- Test the fine-tuned LLM agents on complex tool use tasks
- Compare the performance of GOAT-trained LLM agents with traditional zero-shot evaluation methods
Who Needs to Know This
AI engineers and researchers can benefit from this framework to develop more effective LLM agents, while product managers can utilize it to enhance their AI-powered products
Key Insight
💡 The GOAT framework enables fine-tuning LLM agents without human annotation, improving their ability to reason and interact with complex tools
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🚀 Introducing GOAT: a novel training framework for goal-oriented LLM agents with tools! 🤖
Key Takeaways
Learn how to fine-tune LLM agents for goal-oriented tool use with the GOAT framework, improving their ability to reason and interact with complex tools
Full Article
Title: GOAT: A Training Framework for Goal-Oriented Agent with Tools
Abstract:
arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning capabilities, smaller open-source models remain ineffective at complex tool use. To address this limitation, we propose a novel training framework GOAT, that enables fine-tuning LLM agents without human annotation. GOAT automatically synthesizes goal-oriented API execution data from API documents u
Abstract:
arXiv:2510.12218v2 Announce Type: replace Abstract: Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning capabilities, smaller open-source models remain ineffective at complex tool use. To address this limitation, we propose a novel training framework GOAT, that enables fine-tuning LLM agents without human annotation. GOAT automatically synthesizes goal-oriented API execution data from API documents u
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