paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
QZ

Qin Zhou

ACL 2026×2强化学习与后训练对齐与偏好社会与人本效率与系统可解释性
新星分新星
67
一作贡献30
核心/参与12
方向广度10
合作网络9
1 一作 · 2 篇 · ACL 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning

Qin Zhou · Guoyan Liang · Qianyi Yang · Jingyuan Chen · Sai Wu · Chang Yao · Zhe Wang

摘要

Recent reinforcement learning (RL) approaches have advanced radiology report generation (RRG), yet two core limitations persist: (1) report-level rewards offer limited evidence-grounded guidance for clinical faithfulness; and (2) current methods lack an explicit self-improving mechanism to align...

强化学习与后训练对齐与偏好社会与人本OpenReview 项目页
作者位次 #2/7ACL 2026Findings· Findings of ACL

Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning

Ganghao Liu · Qin Zhou · Zhe Wang · Xuehan Lu · Haihua Huang · Yunfei Tong · Heng Tian

摘要

Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specializ...

效率与系统可解释性强化学习与后训练OpenReview 项目页

常见合作者