AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

TL;DR

The empirical results show that AdaSwitch effectively improves the performance of the local agent, and sometimes achieves competitive results compared to the cloud agent while utilizing much less computational overhead.

Abstract

Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs for lower computational cost. The former option is typically costly and inefficient, while the latter usually fails to deliver satisfactory performance for reasoning steps requiring deliberate thought processes. In this work, we propose a novel LLM utilization paradigm that facilitates the collaborative operation of large cloud-based LLMs and smaller local-deployed LLMs. Our framework comprises two primary modules: the local agent instantiated with a relatively smaller LLM, handling less complex reasoning steps, and the cloud agent equipped with a larger LLM, managing more intricate reasoning steps. This collaborative processing is enabled through an adaptive mechanism where the local agent introspectively identifies errors and proactively seeks assistance from the cloud agent, thereby effectively integrating the strengths of both locally-deployed and cloud-based LLMs, resulting in significant enhancements in task completion performance and efficiency. We evaluate AdaSwitch across 7 benchmarks, ranging from mathematical reasoning and complex question answering, using various types of LLMs to instantiate the local and cloud agents. The empirical results show that AdaSwitch effectively improves the performance of the local agent, and sometimes achieves competitive results compared to the cloud agent while utilizing much less computational overhead.

Figures (5)

• Figure 1: A brief illustration of AdaSwitch framework, in which local agent and cloud agent alternate to collaboratively fulfill the given question.
• Figure 2: The illustration of AdaSwitch. 1) Self-Practicing: The local agent practices on the training dataset to brain the basic reasoning ability. 2) Collaborative Examination: The local agent undertakes an exam to expose its weakness, during which the cloud agent will be utilized to correct the mistakes. 3) Reflective Learning: The local agent is trained on the mistake-correction trajectories generated in the second stage.
• Figure 3: We conduct an ablation study by removing the cloud agent, self-reflection, and reflective learning.
• Figure 4: Cost-Effectiveness Analysis. We conduct experiments on the GSM8K dataset. From left to right, the cost of the methods gradually increases. From the bottom to the top, the accuracy of the method increases.
• Figure 5: Case studies of solving Mathematical Reasoning and Complex QA Reasoning problems. Blue text indicates the generation from the local agent while Red text indicates the generation from the cloud agent