Jingbo Zhu

RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction

Chenglong Wang, Ziming Zhu, Yifu Huo, et al.

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

Kaiyang Ye, Yuan Ge, Junxiang Zhang, et al.

DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast

Zhengkun Ge, Xiaoqian Liu, Haoran Zhang, et al.

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

Yuchun Fan, Bei Li, Peiguang Li, et al.

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment

Yingfeng Luo, Hongyu Liu, Dingyang Lin, et al.

DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering

Yilin Wang, Yuchun Fan, Jiaoyang Li, et al.

This paper addresses the challenge of answering complex multi-step questions across multiple languages using AI systems. The researchers created new multilingual benchmarks for testing these systems and developed DaPT, a framework that processes questions in both the original language and English simultaneously to better retrieve relevant information and generate answers. Their approach significantly outperforms existing systems, achieving 18.3% improvement on the hardest benchmark.