OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
arXiv:2605.29829v1 Announce Type: cross Abstract: Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient paradigm for automated optimization. However, existing methods still exhibit limited generalization: they are sensitive to superficial narrative variations, reuse experience mainly at the case level, and struggle to adapt to shifted or emerging problem types. We propose OptSkills, an archetype-centric skill learning and reasoning agent system for optimization modeling and solving. To improve robust
The proliferation of LLMs creates a pressing need for more robust and generalizable AI agent systems capable of automated problem-solving beyond superficial narrative variations.
This development addresses a critical limitation in current AI agent generalization, paving the way for more reliable and adaptable autonomous systems that can handle complex, evolving optimization challenges.
AI systems will become less sensitive to minor narrative differences and more capable of learning 'archetypal skills,' significantly expanding their applicability and reducing the need for constant re-training for similar problems.
- · AI Agent developers
- · Industries with complex optimization problems
- · Researchers in AI generalization
- · SaaS platforms adopting advanced automation
- · Companies relying on brittle, highly specialized AI solutions
- · Manual optimization consultants
- · Developers of custom, one-off AI solutions
Increased efficiency and automation in tasks requiring complex optimization and problem-solving.
Acceleration of AI adoption in new domains as foundational reliability and adaptability improve.
Reconfiguration of white-collar work due to the capacity for autonomous agents to handle a wider array of strategic and operational challenges.
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Read at arXiv cs.LG