How can I best combine large and small LLMs for web agent tasks?
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
This paper introduces AgentSymbiotic, a framework for web agents that uses large and small language models (LLMs) cooperatively. Large LLMs generate high-quality web interaction data, which is then used to train smaller, faster LLMs. These smaller LLMs explore the web more broadly, generating additional diverse data, including edge cases missed by the larger models. This new data is then fed back to the large LLM, creating a continuous improvement loop. Key innovations include speculative data synthesis to reduce bias during LLM training and multi-task learning to improve the smaller LLM's reasoning abilities. A hybrid mode improves privacy by handling sensitive data locally with a small LLM. Experiments show AgentSymbiotic outperforms previous state-of-the-art approaches on the WEBARENA benchmark.