5 papers
PerSPL: Personalized Semi-supervised Preference Learning
Xiaoan Xu, Tingzhou Wei, Juncheng Dong +3
Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user. At the same tim…
When Right Meets Wrong: Bilateral Context Conditioning with Reward-Confidence Correction for GRPO
Yu Li, Tian Lan, Zhengling Qi
Group Relative Policy Optimization (GRPO) has emerged as an effective method for training reasoning models. While it computes advantages based on group mean, GRPO treats each outpu…
PASTA: A Unified Framework for Offline Assortment Learning
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We study a broad class of assortment optimization problems in an offline and data-driven setting. In such problems, a firm lacks prior knowledge of the underlying choice model, and…
Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning
Korel Gundem, Juncheng Dong, Dennis Zhang +2
In-Context Learning (ICL) allows Large Language Models (LLMs) to adapt to new tasks with just a few examples, but their predictions often suffer from systematic biases, leading to…
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand
Korel Gundem, Zhengling Qi
In this paper, we study the offline sequential feature-based pricing and inventory control problem where the current demand depends on the past demand levels and any demand exceedi…