LiteCoOp: Lightweight Multi-LLM Shared-Tree Reasoning for Model-Serving Compiler Optimizations
arXiv:2602.01935v2 Announce Type: replace Abstract: LLM-guided compiler optimization has recently shown promise, but existing approaches rely on a single large LLM throughout search, making them expensive and excluding smaller models. We pose the research question: whether heterogeneous LLMs can collaborate during compiler optimization while reducing compilation cost below optimization guided by a single large LLM. Crucially, this must be achieved without introducing overhead from agentic frameworks, which would run counter to the goal of lower compilation cost. To achieve these competing obje
The increasing cost and computational demands of large language models for specialized tasks like compiler optimization are driving research into more efficient, collaborative approaches.
This development could significantly reduce the cost and computational burden of integrating AI into complex software development, making LLM-guided optimization more accessible and scalable.
The paradigm shifts from reliance on a single, monolithic LLM for complex tasks towards a more distributed, heterogeneous model where smaller, specialized LLMs collaborate efficiently.
- · AI model-serving companies
- · Software developers
- · Cloud computing providers (potentially lower egress costs)
- · Smaller LLM developers
- · Monolithic LLM providers (for optimization tasks)
- · Companies with inefficient model-serving architectures
Reduced computational costs and increased efficiency in LLM-guided compiler optimizations.
Accelerated adoption of AI in software engineering and development workflows due to lower barriers to entry.
The development of a robust ecosystem for specialized, collaborative LLMs and modular AI architectures across various industries, not just compilation.
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Read at arXiv cs.LG