AgentFly: Fine-Tuning AI Agents Without Fine-Tuning LLMs

Author(s): MKWriteshere

Originally published on Towards AI.

How memory-based learning achieves 87% accuracy on GAIA benchmarks without costly parameter updates

Every attempt to make AI agents smarter hits the same wall: computationally intensive gradient updates that cost millions and risk erasing everything the model learned before.

In the article, the author introduces AgentFly, a novel approach that fine-tunes AI agents using memory-based learning, avoiding the need for expensive and risky parameter updates. Through innovative techniques, AgentFly allows AI to learn continuously from experiences, akin to human learning, improving efficiency and performance while reducing computational costs. The article discusses the challenges faced by traditional methods, highlights the advantages of memory-based learning, and showcases impressive benchmarks that demonstrate AgentFly’s effectiveness compared to other models.

Read the full blog for free on Medium.

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Published via Towards AI