LLMs Can Design Near-Optimal OR Algorithms

arXiv:2608.27296 · cs.AI, cs.LG · Submitted 2026-08-27 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-08-27

Updated: 2026-09-08

Project page: https://infotheorylab.github.io/beer-game

Terminology

• Benchmark — 100× in this paper · explained in 2 episodes
• Reinforcement Learning — 12× in this paper · explained in 4 episodes
• alpha — 7× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Value Iteration — 6× in this paper
• Deep Reinforcement Learning — 5× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Combinatorial Optimization — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 3× in this paper
• AI Agents — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Resource Constraints — 2× in this paper · explained in 3 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes

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