arXiv:2606.05464v1 Announce Type: new Abstract: Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making. Many real-world tasks require finding a high-value feasible plan among many valid alternatives. We introduce OPT*, a scalable family of optimization-style tasks for training and evaluating LLM step-by-step optimization-like reasoning along a complexity axis: each task provides a feasibility checker and evaluator, while a complexity parameter expands the search space without requiring new human labels. Thi
The continuous development in LLMs is pushing the boundaries of their reasoning capabilities, making advanced optimization a natural next frontier.
Improving LLM step-by-step optimization reasoning can unlock new capabilities for complex real-world problem-solving across various industries.
LLMs are moving beyond simple verifiability to tackle more complex, multi-alternative optimization problems, expanding their applicability.
- · AI developers
- · Automation software sector
- · Enterprises with complex planning needs
- · Scientific research
- · Tasks requiring manual complex optimization
- · Specialized optimization software with limited LLM integration
LLMs will become more effective at complex decision-making and planning tasks.
This improved capability will accelerate automation in white-collar roles requiring strategic optimization.
The enhanced decision-making of AI could lead to more efficient allocation of resources across entire industrial sectors.
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Read at arXiv cs.AI