arXiv AI June 8, 2026 By Chenyu Huang, Jianghao Lin, Zhengyang Tang, Bo Jiang, Ruoqing Jiang, Benyou Wang, Lai Wei

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees

arXiv:2605. 00369v4 Announce Type: replace-cross Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 10

Automated Design of Inventory Policy with Large Language Models: An Exploratory Study

arXiv:2609.08071v1 Announce Type: new Abstract: Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize th...

By Fenghua Yang, Preet Baxi, Yi Zhang, Stefanus Jasin, Yanzhe Lei, Mo Liu, Parshan Pakiman

llms benchmarks

arXiv Machine Learning

2d ago

Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making

arXiv:2504.09192v5 Announce Type: replace Abstract: The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncer...

By Zhiyong Wang

llms reinforcement-learning robotics fine-tuning safety

arXiv AI

Jun 6

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

arXiv:2606. 06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking.

By Amandeep Kaur, Gyan Prakash

reinforcement-learning benchmarks

arXiv AI

2d ago

Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

arXiv:2609.33289v2 Announce Type: replace Abstract: Autonomous large language model (LLM) agents operating in multi-product markets must make sequential decisions under information asymmetry and reso...

By Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi, Thorsten Joachims

llms agents reinforcement-learning

arXiv AI

Jun 15

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.

By Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Aug 21

SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning

arXiv:2608. 19842v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become a critical stage in the post-training of large language models.

By Dayang Liang, Lang Feng, Bo An, Yunlong Liu

llms agents reinforcement-learning

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Related stories

Automated Design of Inventory Policy with Large Language Models: An Exploratory Study

Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

Rethinking the Trust Region in LLM Reinforcement Learning

SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning