LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs

• URL: http://arxiv.org/abs/2605.10401v1
• Date: Mon, 11 May 2026 11:41:54 GMT
• Title: LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs
• Abstract summary: LLM4Branch is a novel framework that automates the discovery of efficient branching policies.<n>Experiments on standard MILP benchmarks demonstrate that LLM4Branch establishes a new state-of-the-art among CPU-based methods.
• Score: 8.092117175018378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient branching policies are essential for accelerating Mixed Integer Linear Programming (MILP) solvers. Their design has long relied on hand-crafted heuristics, and now machine learning has emerged as a promising paradigm to automate this process. However, existing learning-based methods are often hindered by their dependence on expensive expert demonstrations and the gap between training objectives and the solver's end-to-end performance. In this work, we propose LLM4Branch, a novel framework that leverages Large Language Models (LLMs) to automate the discovery of efficient branching policies. Specifically, the discovered policy is an executable program with a program skeleton generated by the LLM and a parameter vector, which is optimized via a zeroth-order method over a few instances with their end-to-end performance feedback. Extensive experiments on standard MILP benchmarks demonstrate that LLM4Branch establishes a new state-of-the-art among CPU-based methods and achieves performance competitive with advanced GPU-based models. Codes are available at https://github.com/hzn18/LLM4Branch.

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