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Ting-En Lin

ACL 2026×2ICLR 2026×2对齐与偏好强化学习与后训练推理评测与基准社会与人本
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0 一作 · 4 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

4
作者位次 #4/13ACL 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 #3/6ACL 2026Findings· Findings of ACL

Reasoning-Guided Exploration for Online DPO

Zetian Hu · Shunyu Liu · Ting-En Lin · Fei Huang · Yongbin Li · Dacheng Tao

摘要

Recent work has aimed to enhance the reasoning capabilities of language models, but these methods are often limited to domains with objectively verifiable answers. To overcome this limitation, we introduce Reasoning-Guided Exploration for Online DPO (RGE-DPO), a novel self-play framework designed...

对齐与偏好推理评测与基准OpenReview 项目页

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