Jiongfeng Fang — accepted AI-conference papers

2026

PSPO: Prompt-Level Prioritization and Experience-Weighted Smoothing for Efficient Policy Optimization

Xinxin Zhu, Ying He, Haowen Hou, Ruichong Zhang, Nianbo Zeng, Yulin Peng +2

AAAI 2026technical

Reinforcement Fine-tuning (RFT) methods such as Group Relative Policy Optimization (GRPO) have demonstrated strong capabilities in aligning Large Language Models with human preferences. However, these approaches often suffer from limited data efficiency, necessitating extensive on-policy rollouts to

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2024

OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations

Yao Shu, Jiongfeng Fang, Ying Tiffany He, Fei Yu

NeurIPS 2024poster

First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence.…

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