Small Language Models: Future of Agentic AI

What this episode covers

This research paper proposes that small language models (SLMs) are the future of agentic AI, challenging the current reliance on large language models (LLMs). The authors argue that SLMs are sufficiently powerful, more operationally suitable, and more economical for the repetitive, specialized tasks common in AI agents. While acknowledging the current dominance and investment in LLMs, the paper provides an algorithm for converting LLM-centric agents to SLM-first architectures, highlighting the significant economic and operational benefits of this shift. It also addresses counterarguments regarding LLM general understanding and the economics of centralized inference, advocating for heterogeneous agentic systems where SLMs handle most tasks, with LLMs used sparingly for complex reasoning.

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