Adaptive Self-improvement LLM Agentic System for ML Library Development (2502.02534v1)

Abstract: ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing these high-performance ML libraries is challenging because it requires expert knowledge of ML algorithms and the ASPL. LLMs, on the other hand, have shown general coding capabilities. However, challenges remain when using LLMs for generating ML libraries using ASPLs because 1) this task is complicated even for experienced human programmers and 2) there are limited code examples because of the esoteric and evolving nature of ASPLs. Therefore, LLMs need complex reasoning with limited data in order to complete this task. To address these challenges, we introduce an adaptive self-improvement agentic system. In order to evaluate the effectiveness of our system, we construct a benchmark of a typical ML library and generate ASPL code with both open and closed-source LLMs on this benchmark. Our results show improvements of up to $3.9\times$ over a baseline single LLM.

• The paper introduces an adaptive self-improvement LLM agent system using experiential learning to generate code for ML libraries in difficult architecture-specific languages (ASPLs).
• Evaluations show the system achieves up to 96% task completion and performance gains up to 3.9 times over a single LLM baseline on an ML operator benchmark.
• This work provides a practical tool for accelerating ML library development for domain-specific hardware and advances research on LLM capabilities in specialized coding tasks.

Adaptive Self-improvement LLM Agentic System for ML Library Development: An Expert Overview

The paper "Adaptive Self-improvement LLM Agentic System for ML Library Development" addresses the significant challenge of developing ML libraries using architecture-specific programming languages (ASPLs), particularly for domain-specific architectures (DSAs). The difficulty arises from the need for deep expertise in both ML algorithms and the intricacies of ASPLs, compounded by the fact that ASPLs frequently evolve with hardware advancements, providing limited code examples for developers. LLMs, despite their prowess in general coding tasks, struggle with the highly specialized nature of ASPLs.

To overcome these challenges, the authors introduce an innovative adaptive self-improvement agentic system, designed to enhance LLM capabilities in generating ASPL code. The system operates by continuously evolving LLM agents using an experiential learning methodology that mimics human learning processes. Through a cycle of parallel sampling, the system filters high-quality responses, stratifies experiences by difficulty, and selects demonstrations adaptively to optimize agent learning. This cycle not only facilitates the development of more efficient ML libraries but also minimizes the need for extensive human involvement beyond initial setup.

Notably, the research employs Streaming Tensor Programs (STeP) as the target ASPL for their experiments—a language catering to next-generation reconfigurable dataflow architectures. Given STeP's limited presence in existing training data, its use serves as a rigorous testbed for the system's capacity to handle emergent ASPLs.

The system's performance was assessed against a custom benchmark of common ML operators. Results indicate substantial improvements, with task completion rates reaching up to 96% and performance gains up to 3.9 times compared to a single LLM baseline. The adaptive learning algorithm demonstrates that prioritizing "hard-earned" experiences, obtained through difficult task completion, leads to more efficient performance enhancement than mixed-experience strategies. Furthermore, the structural intermediate representation used by the system improves performance by facilitating a more efficient interface between users, LLM agents, and the code generator.

These findings have several implications. Practically, the system provides a robust tool for developers tasked with creating ML libraries, potentially accelerating the deployment of DSAs in various applications. Theoretically, the work extends the understanding of LLM capabilities in specialized domains, underscoring the importance of adaptive learning mechanisms to augment reasoning abilities when data is scarce or highly specialized.

For future research, expanding this approach to accommodate a broader range of ASPLs and further refining the adaptive learning algorithms could lead to even more versatile systems. Moreover, exploring the integration of this system with other AI models or frameworks could enhance its application scope. As the field of AI continues to evolve, these advancements may set the groundwork for automated, self-improving systems capable of supporting complex software development landscapes without the immediate need for human expertise in every domain-specific detail.