Patched MOA: optimizing inference for diverse software development tasks (2407.18521v2)

Abstract: This paper introduces Patched MOA (Mixture of Agents), an inference optimization technique that significantly enhances the performance of 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.

A Comprehensive Analysis of Patched MOA for LLM Inference Optimization

In the paper "Patched MOA: optimizing inference for diverse software development tasks," Asankhaya Sharma introduces the Patched MOA (Mixture of Agents) technique, aimed at optimizing the inference of LLMs for various software development tasks. This work investigates alternative inference optimization techniques to elevate the performance of smaller LLMs, thereby offering cost-efficient solutions without necessitating larger models.

The paper undertakes a comparative evaluation of three inference optimization algorithms: Best of N (bon), Mixture of Agents (moa), and Monte Carlo Tree Search (mcts), focusing on their application in software development workflows. The findings reveal that Patched MOA enhances the performance of smaller models such as gpt-4o-mini, allowing them to outperform larger models like gpt-4-turbo on the Arena-Hard-Auto benchmark, achieving a notable 15.52% improvement at a substantially reduced cost.

Methodological Exploration

The core methodological exploration entails the application and evaluation of three distinct optimization techniques during inference:

1. Best of N (bon): This technique involves generating multiple responses from a model and selecting the highest-scoring response using self-generated scores. The approach is computationally inexpensive with a 4x call and cost ratio, yielding modest performance gains.
2. Mixture of Agents (moa): Inspired by Together AI's work, this approach extends the inference process by including critiques of initial responses and synthesizing a final response. The moa technique significantly improves model performance, achieving an 85.6 score in the Arena-Hard-Auto benchmark, surpassing even the gpt-4-turbo at a reasonable computational overhead of 3x calls and 8x time.
3. Monte Carlo Tree Search (mcts): This method deploys a tree-based search strategy for exploring various dialogue states, maximizing the quality of responses. Despite achieving competitive improvements, mcts incurs higher computational costs with 9x API calls and 32x cost, thus limiting its practical applicability.

Evaluation and Results

The evaluation uses the Arena-Hard-Auto benchmark, a stringent measure closely aligned with real-world chatbot performance. Results demonstrate that Patched MOA, utilizing the moa technique, outstrips larger commercial models by three points while maintaining economical resource use. Moreover, diverse "patchflows" in software development tasks further affirm the superiority of the moa approach, with significant enhancements observed across varied tasks such as AutoFix, PRReview, and others.

Implications and Future Directions

The implications of Patched MOA are significant for both theoretical advancements and practical applications in LLM deployment. For practitioners, the model-agnostic nature of Patched MOA allows seamless integration into existing workflows, yielding enhanced performance without altering model architecture or prompts. Theoretically, this paper contributes to the burgeoning field of inference optimization by validating that careful application of agent-based techniques can lead to performance leaps at reduced computational expenses.

Speculating on future developments, the progress demonstrated by Patched MOA opens avenues for further research into optimizing the balance between cost and performance in LLMs. Subsequent work could explore more dynamic agent configurations or incorporate adaptive, context-aware mechanisms that customize inference strategies according to task-specific requirements.

In conclusion, Patched MOA presents a compelling case for rethinking inference optimization strategies in LLMs, advocating for intelligent design choices that extend the capabilities of smaller models to rival their more resource-intensive counterparts. This approach not only challenges the hegemony of larger models but also aligns with sustainable computing paradigms by reducing the carbon footprint of AI operations. Researchers and practitioners stand to gain considerable insights by exploring the methodologies and evaluations presented within this work.