Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization
arXiv:2605.25954v1 Announce Type: new Abstract: Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution efficiency of tensor programs remains challenging due to the need for precise, composable transformation decisions. Recent LLM-guided approaches frame tensor program optimization as an iterative decision process, but existing datasets provide only end-to-end optimized program pairs using token-inefficient representations, lacking verifiable step-level supervision and interpretability. As a result, LLMs struggle to make reliable single-step decision
The rapid advancement of LLMs makes their application to complex optimization problems, like tensor program efficiency, a natural next frontier, requiring focused datasets for nuanced training. The bottleneck of efficient hardware utilization for AI workloads grows daily, pushing for solutions that can automate and enhance performance.
This research provides a crucial step towards more efficient and reliable LLM-guided optimization of tensor programs, which are fundamental to AI computation. Improved tensor program optimization significantly impacts the overall performance and cost of AI models, thus affecting the accessibility and scalability of AI technologies.
The introduction of a 'grounded, step-level dataset with chain-of-thought reasoning' provides a new training methodology for LLMs to generate more precise and verifiable optimization decisions. This will improve the capability of LLMs to automate and optimize low-level software decisions for AI hardware.
- · AI hardware manufacturers
- · Cloud computing providers
- · Large language model developers
- · AI research institutions
- · Manual low-level optimization specialists
- · Companies with inefficient AI infrastructure
LLMs will become more adept at automating complex, multi-step optimization tasks in computing, reducing the need for human experts in certain specialized roles.
Increased computational efficiency derived from LLM-optimized tensor programs could lower the cost of training and deploying sophisticated AI models, democratizing access to powerful AI capabilities.
The enhanced efficiency might accelerate the development of more complex AI models, particularly in areas currently constrained by computational resources, opening new frontiers in AI research and application.
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Read at arXiv cs.LG