小模型用工具和协作，能大幅提高性能。
Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms
- 用工具和多智能体系统提升小模型能力
- 单智能体表现最佳，多智能体增开销但收益低
- 适合资源受限场景的高效部署
尽管大语言模型能力强大，但其高昂的计算成本、延迟和隐私风险限制了在真实应用中的广泛部署。参数少于100亿的小语言模型（SLMs）提供了一种有前景的替代方案；然而，其知识和推理能力的固有局限性削弱了实际效果。现有研究主要通过缩放定律或微调策略提升小模型，却忽视了使用智能体范式（如工具调用和多智能体协作）系统性弥补小模型缺陷的潜力。为此，本文首次对<100亿参数的开源模型在三种范式下的表现进行大规模综合研究：(1) 基础模型，(2) 配备工具的单智能体，(3) 具备协作能力的多智能体系统。结果表明，单智能体系统在性能与成本间取得最佳平衡，而多智能体架构虽增加开销但收益有限。研究强调了面向智能体的设计对资源受限环境中高效可信部署的重要性。
原文摘要 · Abstract (English)
Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widespread deployment in real-world applications. Small Language Models (SLMs) with fewer than 10 billion parameters present a promising alternative; however, their inherent limitations in knowledge and reasoning curtail their effectiveness. Existing research primarily focuses on enhancing SLMs through scaling laws or fine-tuning strategies while overlooking the potential of using agent paradigms, such as tool use and multi-agent collaboration, to systematically compensate for the inherent weaknesses of small models. To address this gap, this paper presents the first large-scale, comprehensive study of <10B open-source models under three paradigms: (1) the base model, (2) a single agent equipped with tools, and (3) a multi-agent system with collaborative capabilities. Our results show that single-agent systems achieve the best balance between performance and cost, while multi-agent setups add overhead with limited gains. Our findings highlight the importance of agent-centric design for efficient and trustworthy deployment in resource-constrained settings.
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