Lulu Wu — accepted AI-conference papers

2026

DRIVE: Best Data Scheduling Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation

Speed Zhu, Chuheng Zhang, Jianwei Cai, Guang Chen, Lulu Wu, Xiaolong Xu +3

ICML 2026poster

Recent success of large reasoning models (such as OpenAI o1 and DeepSeek R1) have spurred a resurgence of interest in reinforcement learning from verifiable rewards (RLVR). However, progress is still largely driven by RL algorithm design, while data scheduling -- the data-side decisions that determi…

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2026

LaSeR: Reinforcement Learning with Last-Token Self-Rewarding

Wenkai Yang, Weijie Liu, Ruobing Xie, Yiju Guo, Lulu Wu, Saiyong Yang +1

ICLR 2026poster

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a core paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). To address the lack of verification signals at test time after RLVR, prior studies incorporate the training of model's self-verifica…

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