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

Zhuoyang Wu

ACL 2026×2推理多模态对齐与偏好强化学习与后训练
新星分新星
66
一作贡献30
核心/参与12
方向广度8
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
作者位次 #2/9ACL 2026Poster· Natural Language Processing

Long-Chain Reasoning Distillation via Adaptive Prefix Alignment

Zhenghao Liu · Zhuoyang Wu · Xinze Li · Yukun Yan · Shuo Wang · Zulong Chen · Yu Gu · Ge Yu · Maosong Sun

摘要

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in solving complex mathematical problems. Recent studies show that distilling long reasoning trajectories can effectively enhance the reasoning performance of small-scale student models. However, teache...

推理对齐与偏好多模态OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection

Zhuoyang Wu · Xinze Li · Zhenghao Liu · Yukun Yan · Zhiyuan Liu · Minghe Yu · Cheng Yang · Yu Gu · Ge Yu · Maosong Sun

摘要

Large Language Models (LLMs) have exhibited strong reasoning capabilities and achieved remarkable performance in mathematical problem-solving tasks. Recently, distilling reasoning ability from long-form Chains-of-Thought (CoTs) has emerged as a promising approach for enhancing Small Language Mode...

推理强化学习与后训练多模态OpenReview 项目页

常见合作者