Patched MOA: optimizing inference for diverse software development tasks
This provides a cost-effective solution for software developers to enhance LLM performance without fine-tuning or using larger models, though it is incremental as it builds on existing inference optimization techniques.
The paper tackles the problem of optimizing inference for large language models (LLMs) in software development tasks, resulting in a 15.52% performance improvement on the Arena-Hard-Auto benchmark for the gpt-4o-mini model, allowing it to outperform larger models like gpt-4-turbo at lower cost.
This paper introduces Patched MOA (Mixture of Agents), an inference optimization technique that significantly enhances the performance of large language models (LLMs) across diverse software development tasks. We evaluate three inference optimization algorithms - Best of N, Mixture of Agents, and Monte Carlo Tree Search and demonstrate that Patched MOA can boost the performance of smaller models to surpass that of larger, more expensive models. Notably, our approach improves the gpt-4o-mini model's performance on the Arena-Hard-Auto benchmark by 15.52%, outperforming gpt-4-turbo at a fraction of the cost. We also apply Patched MOA to various software development workflows, showing consistent improvements in task completion rates. Our method is model-agnostic, transparent to end-users, and can be easily integrated into existing LLM pipelines. This work contributes to the growing field of LLM optimization, offering a cost-effective solution for enhancing model performance without the need for fine-tuning or larger models. Our implementation is open-source and available at https://github.com/codelion/optillm.