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

Researchers introduce MiniOpt, a reinforcement learning framework that enables compact language models (3B parameters) to solve diverse optimization problems efficiently without requiring large supervised datasets or expensive expert annotations. The approach uses a hierarchical reward function and structured decomposition strategy, achieving competitive performance compared to larger models while significantly reducing training overhead.

MiniOpt addresses a fundamental challenge in AI research: developing specialized models that perform well across diverse tasks without the computational and financial burden of large-scale training datasets. The framework's innovation lies in its 'reasoning-to-model-and-solve' paradigm, which breaks down complex optimization tasks into manageable components—structured modeling and solver generation—enabling more efficient learning with limited resources.

The breakthrough centers on OptReward, a hierarchical reward function that evaluates both problem formulation quality and solution correctness simultaneously. This eliminates the need for expensive expert demonstrations and intermediate step verification that typically plague optimization-focused AI systems. The reinforcement learning approach allows the model to learn from its own problem-solving experiences, making the training process more resource-efficient and scalable.

For the AI and machine learning industry, this represents meaningful progress toward democratizing advanced AI capabilities. Models with fewer than 10 billion parameters achieving highest average solving accuracy across multiple problem types suggests that elegant algorithmic design can compete with brute-force scaling. This has implications for organizations with limited computational budgets and those seeking more sustainable AI development practices.

The competitive performance of MiniOpt-3B creates opportunities for deployment in resource-constrained environments—edge devices, smaller enterprises, and research institutions with limited infrastructure. As optimization problems pervade finance, logistics, engineering, and numerous other domains, compact specialized models could accelerate practical AI adoption. The open-source release of code enables rapid community iteration and refinement of these techniques.

• →MiniOpt achieves state-of-the-art optimization solving with just 3 billion parameters, reducing training resource requirements significantly
• →The hierarchical OptReward function eliminates costly expert annotations by jointly evaluating problem formulation and solution quality
• →Compact optimization models enable deployment in resource-constrained environments where larger models are impractical
• →Reinforcement learning combined with structured task decomposition provides an effective alternative to supervised learning at scale
• →Open-source release accelerates community research on efficient, specialized language models