Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms
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 p...
| Authors | Xinlin Wang, Mats Brorsson |
|---|---|
| Primary category | cs.CL |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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