5 papers
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
Chenyu Huang, Jianghao Lin, Zhengyang Tang +4
We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based e…
LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models
Chenxing Wei, Jiazhen Kang, Hong Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requiring correctness like mathemati…
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Chenxing Wei, Hong Wang, Ying He +4
Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…
Generative Expansion of Small Datasets: An Expansive Graph Approach
Vahid Jebraeeli, Bo Jiang, Hamid Krim +1
Limited data availability in machine learning significantly impacts performance and generalization. Traditional augmentation methods enhance moderately sufficient datasets. GANs st…
Koopcon: A new approach towards smarter and less complex learning
Vahid Jebraeeli, Bo Jiang, Derya Cansever +1
In the era of big data, the sheer volume and complexity of datasets pose significant challenges in machine learning, particularly in image processing tasks. This paper introduces a…