AlphaInventory: LLM-Driven Inventory Policy Evolution with Deployment Guarantees
A new framework called AlphaInventory uses large language models to evolve inventory policies in online, non-stationary environments. It is motivated by AlphaEvolve, which excels at static problems but not dynamic inventory settings. AlphaInventory trains an LLM via reinforcement learning, incorporating demand data and numerical/textual features. It generates white-box inventory policies with statistical safety guarantees based on confidence-interval certification. A unified theoretical interface connects training, inference, and deployment. The approach aims to provide robust, adaptable inventory management for changing conditions.
Key facts
- AlphaInventory is an end-to-end inventory-policy evolution and inference framework.
- It uses large language models trained with reinforcement learning.
- The framework incorporates demand data and features beyond demand.
- It generates white-box inventory policies with statistical safety guarantees.
- A unified theoretical interface connects training, inference, and deployment.
- The work is motivated by AlphaEvolve, which is suited for static problems.
- AlphaInventory targets online, non-stationary environments.
- The framework is grounded in confidence-interval-based certification.
Entities
—