MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

📰 ArXiv cs.AI

Learn how MiniOpt enables optimization-oriented LLMs to solve general optimization problems with limited resources and training data

advanced Published 25 Jun 2026
Action Steps
  1. Implement MiniOpt to model and solve general optimization problems
  2. Train MiniOpt using limited resources and datasets
  3. Evaluate MiniOpt's performance on diverse optimization problems
  4. Compare MiniOpt's results with existing optimization approaches
  5. Apply MiniOpt to real-world optimization problems with limited resources
Who Needs to Know This

Optimization researchers and engineers working with large language models can benefit from MiniOpt's efficient approach to solving diverse optimization problems

Key Insight

💡 MiniOpt enables optimization-oriented LLMs to achieve strong optimization generalization with limited training resources

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🚀 MiniOpt: Efficiently solving general optimization problems with limited resources! 🤖

Full Article

Title: MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

Abstract:
arXiv:2606.25832v1 Announce Type: cross Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs). Existing approaches typically rely on large-scale supervised datasets, costly reasoning annotations, and expensive intermediate step verification, resulting in substantial training overhead. To address these challenges, we propose MiniOpt,
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