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

Xiang Huang

ACL 2026×2AAAI 2026强化学习与后训练评测与基准对齐与偏好推理多模态
新星分贡献者
57
一作贡献0
核心/参与22
方向广度13
合作网络12
0 一作 · 3 篇 · ACL 2026 / AAAI 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

3
作者位次 #3/13ACL 2026Poster· Natural Language Processing

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity

Feiteng Fang · Dingwei Chen · Xiang Huang · Ting-En Lin · Yuchuan Wu · Xiong Liu · Jing Ye · Ziqiang Liu · Haonan Zhang · Liang Zhu · Hamid Alinejad-Rokny · Min Yang · Yongbin Li

摘要

Currently, most reinforcement learning tasks focus on domains like mathematics and programming, where verification is relatively straightforward. However, in subjective tasks such as role-playing, alignment techniques struggle to make progress, primarily because subjective reward modeling using t...

对齐与偏好强化学习与后训练评测与基准OpenReview 项目页
作者位次 #2/9ACL 2026Findings· Findings of ACL

T o M -Synth: Scaling Robust Theory of Mind in LLM s via 6,912 Structured Social Units

Guiyang Hou · Xiang Huang · Shangke Lyu · Yuchuan Wu · Weiyao Luo · Xinyu Mei · Yongliang Shen · Weiming Lu · Yongbin Li

摘要

Theory of Mind (ToM), the ability to infer others’ mental states from behavior, is pivotal for developing machines with human-level social intelligence. Existing methods endowing LLMs with ToM fall into two paradigms: training-free methods and those repurposing ToM evaluation benchmarks as traini...

推理强化学习与后训练评测与基准OpenReview 项目页

常见合作者