Reason-to-Learn (R2L): Multi-Agent Knowledge Distillation for Lightweight LLMs in Sentiment Analysis

Abstract

Large Language Models (LLMs) boast remarkable capabilities but face deployment challenges due to computational demands. We introduce Reason-to-Learn (R2L), a novel multi-agent collaborative knowledge distillation framework enabling small LLMs to learn from a distributed system of specialized agent models. Our architecture employs multiple autonomous teacher agents, each with distinct expertise and reasoning capabilities, coordinated by a meta-agent that orchestrates knowledge synthesis and conflict resolution. Unlike prior methods, our flexible four-phase process (Detection, Processing, Rationale Generation, Aggregation) leverages agent-based communication protocols and consensus mechanisms for cross-architecture knowledge transfer, demonstrated primarily on Vietnamese sentiment analysis. Experimental results are definitive: our lightweight R2L-Students (1-1.5B) consistently outperform the individual specialized agents (Qwen32B, Llama70B) and the GPT-4o meta-agent coordinator, especially on complex ABSA tasks. Ablation studies confirm our multi-agent collaborative approach outperformed traditional fine-tuning and single-agent distillation. Furthermore, R2L enhance generalizability of lightweight LLMs: our Vietnamese-trained student achieves strong zero-shot cross-lingual performance on Swedish ABSA (Svensk ABSAbank-Imm), with Krippendorff’s Alpha scores competitive with the specialized agents. R2L offers an efficient path to compact, high-performing specialist models through coordinated multi-agent learning.

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