11h ago
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
★★★★★
significance 3/5
The paper introduces Tandem, a framework that combines Large Language Models (LLMs) with Small Language Models (SLMs) to optimize reasoning-intensive tasks. By using the LLM as a strategic coordinator for the SLM, the method reduces computational costs by approximately 40% while maintaining high performance in math and coding.
Why it matters
Optimizing the hierarchy between LLMs and SLMs offers a viable path toward reducing the massive computational overhead of high-reasoning tasks.
Tags
#llm #slm #reasoning #efficiency #inferenceRelated coverage
- Global South OpportunitiesPivotal Research Fellowship 2026 (Q3): AI Safety Research Opportunity - Global South Opportunities
- arXiv cs.AIAn Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
- arXiv cs.AIPExA: Parallel Exploration Agent for Complex Text-to-SQL
- arXiv cs.AIThe Power of Power Law: Asymmetry Enables Compositional Reasoning
- arXiv cs.AIOn the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation