Zhonghai Wu

Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learning for LLMs faces substantial data scarcity challenges, including the limited availability of high-quality external s...

Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, their performance remains far from satisfactory due to the substantial annotation cost and issues such as model collapse...

Click-through Rate (CTR) prediction is a crucial task in online advertising and recommender systems. Theoretically, proper ensemble of multiple different CTR prediction models can improve the prediction effectiveness. Unfortunately, most of the existing ensemble learning methods for CTR predictio...