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Binghai Wang

ACL 2026×2对齐与偏好评测与基准推理强化学习与后训练检索与知识
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60
一作贡献30
核心/参与5
方向广度10
合作网络6
1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models

Binghai Wang · Yantao Liu · Yuxuan Liu · Tianyi Tang · Shenzhi Wang · Chang Gao · Chujie Zheng · Yichang Zhang · Le Yu · Shixuan Liu · Tao Gui · Qi Zhang · Xuanjing Huang · Bowen Yu · Fei Huang · Junyang Lin

摘要

Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy , which undermines their ability to generalize during RLHF. We introduce Rationale Consistency ,...

对齐与偏好评测与基准推理OpenReview 项目页
作者位次 #3/15ACL 2026Findings· Findings of ACL

MM - D oc- R 1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning

Jiahang Lin · Kai Hu · Binghai Wang · Yuhao Zhou · Zhiheng Xi · Honglin Guo · Shichun Liu · Junzhe Wang · Shihan Dou · Enyu Zhou · Hang Yan · Zhenhua Han · Tao Gui · Qi Zhang · Xuanjing Huang

摘要

Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-R1 , a novel framework that employs an agentic, vision-aware workflow to address long document visual question answer...

强化学习与后训练检索与知识多模态OpenReview 项目页

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