Automates mathematical optimization modeling using reinforcement learning with solver-derived rewards instead of human process supervision.
April 2, 2026
Original Paper
Execution-Verified Reinforcement Learning for Optimization Modeling
arXiv · 2604.00442
The Takeaway
Treats solvers as verifiable reward environments (EVOM), allowing models to adapt to different solver backends (Gurobi, OR-Tools) without costly labeled datasets or process-level labeling.
From the abstract
Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-source LLMs with high inference latency, or fine-tune smaller LLMs using costly process supervision that often overfits to a single solver API. Inspired by reinforcement learning with verifiable rewards, we propose Execution-Verified Optimization Modeling (EVOM), an execution-verified learning framework that treats a mathem