Patched MOA (Mixture of Agents) is an inference optimization technique that enables smaller language models to outperform larger, more expensive models across software development tasks.

• Combines three powerful algorithms: Best of N, Mixture of Agents, and Monte Carlo Tree Search
• Significantly improves performance on diverse software development workflows
• Enables cost-effective deployment of smaller models without sacrificing capability
• Demonstrates practical optimization path as alternative to simply scaling model size

For engineering teams, this research offers a practical approach to implement more efficient LLM solutions with reduced computational requirements while maintaining or improving performance on code-related tasks.

Patched MOA: optimizing inference for diverse software development tasks