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

2026-06-24 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, medium

Summary

MiniOpt is a novel reinforcement learning framework designed to enhance optimization generalization in large language models (LLMs) with limited training resources. It tackles the common issues of extensive supervised datasets, expensive reasoning annotations, and costly intermediate step verification. MiniOpt employs a "reasoning-to-model-and-solve" paradigm, breaking down optimization reasoning into structured optimization modeling and executable solver generation. The framework introduces OptReward, a hierarchical reward function that jointly evaluates problem formulation and solution quality, enabling effective policy learning without expert demonstrations. Additionally, it features an optimization-oriented policy optimization strategy to improve exploration efficiency and stabilize reinforcement learning for compact models. Experiments demonstrate that MiniOpt-3B achieves strong optimization generalization across diverse problem types and domains. The MiniOpt series consistently shows the highest average solving accuracy (SA) for models with fewer than 10B parameters and competitive performance for models exceeding 10B parameters.

Key takeaway

For Machine Learning Engineers developing optimization-oriented LLMs, MiniOpt offers a pathway to achieve strong generalization with limited training resources. You should consider adopting its "reasoning-to-model-and-solve" paradigm and hierarchical reward design to improve model efficiency. This approach allows you to build compact models, like MiniOpt-3B, that perform competitively against larger models, reducing computational overhead and accelerating deployment in resource-constrained environments.

Key insights

MiniOpt is an RL framework that enables compact LLMs to generalize across diverse optimization problems using a "reasoning-to-model-and-solve" paradigm.

Principles

• Decompose optimization reasoning into structured modeling and solver generation.
• Use hierarchical rewards to jointly evaluate formulation and solution quality.
• Optimize policy for exploration efficiency and stable RL in compact models.

Method

MiniOpt employs reinforcement learning to decompose optimization reasoning into structured modeling and executable solver generation. It uses OptReward, a hierarchical reward function, and an optimization-oriented policy optimization strategy for effective policy learning.

In practice

• Develop compact LLMs for general optimization problems.
• Implement hierarchical reward functions for complex task evaluation.
• Apply policy optimization to stabilize RL for smaller models.

Topics

• MiniOpt
• Reinforcement Learning
• Optimization LLMs
• Compact Models
• Optimization Generalization
• Reward Functions

Code references

• Hsiang-1/MiniOpt
• Arthur-Heng/NLGraph

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.