MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models

TL;DR

MAGDi is a structured distillation method that encodes multi-agent reasoning interactions as graphs, enabling smaller language models to improve reasoning performance efficiently and generalize better across tasks.

Contribution

This paper introduces MAGDi, a novel graph-based distillation approach that captures multi-agent interactions, enhancing smaller models' reasoning abilities beyond existing single-teacher methods.

Findings

MAGDi outperforms single and multi-teacher distillation methods on reasoning benchmarks.

It significantly improves out-of-domain generalization.

MAGDi achieves higher efficiency compared to its teacher models.

Abstract

Multi-agent interactions between Large Language Model (LLM) agents have shown major improvements on diverse reasoning tasks. However, these involve long generations from multiple models across several rounds, making them expensive. Moreover, these multi-agent approaches fail to provide a final, single model for efficient inference. To address this, we introduce MAGDi, a new method for structured distillation of the reasoning interactions between multiple LLMs into smaller LMs. MAGDi teaches smaller models by representing multi-agent interactions as graphs, augmenting a base student model with a graph encoder, and distilling knowledge using three objective functions: next-token prediction, a contrastive loss between correct and incorrect reasoning, and a graph-based objective to model the interaction structure. Experiments on seven widely used commonsense and math reasoning benchmarks show that MAGDi improves the reasoning capabilities of smaller models, outperforming several methods that distill from a single teacher and multiple teachers. Moreover, MAGDi also demonstrates an order of magnitude higher efficiency over its teachers. We conduct extensive analyses to show that MAGDi (1) enhances the generalizability to out-of-domain tasks, (2) scales positively with the size and strength of the base student model, and (3) obtains larger improvements (via our multi-teacher training) when applying self-consistency -- an inference technique that relies on model diversity.