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Deqing Yang

ACL 2026×4ICML 2026×2AAAI 2026强化学习与后训练智能体与工具评测与基准推理可解释性
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0 一作 · 7 篇 · ACL 2026 / ICML 2026 / AAAI 2026

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2026 顶会论文

7
作者位次 #6/7ACL 2026Poster· Natural Language Processing

What Makes an Ideal Quote? Recommending “Unexpected yet Rational” Quotations via Novelty

Powei Chang · Jin Xiao · Guanglei Yue · Qianyu He · Yanghua Xiao · Deqing Yang · Jiaqing Liang

摘要

Quotation recommendation enriches writing by suggesting quotations that fit a given context, but prior systems largely focus on topical relevance and overlook what makes quotes memorable. Based on a user study, we find that preferred quotations are often unexpected yet rational, motivating the go...

社会与人本智能体与工具评测与基准OpenReview 项目页
作者位次 #8/10ACL 2026Poster· Natural Language Processing

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning

Ziheng Li · Liu Kang · Feng Xiao · Luxi Xing · Qingyi Si · Zhuoran Li · Weikang Gong · Deqing Yang · Yanghua Xiao · Hongcheng Guo

摘要

Group Relative Policy Optimization (GRPO) has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks. However, standard GRPO employs a coarse-grained credit assignment mechanism that propagates group-level rewards uniformly to to every token in a sequence, neglecti...

强化学习与后训练推理可解释性OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

Confidence Estimation for LLM s in Multi-turn Interactions

Caiqi Zhang · Ruihan Yang · Xiaochen Zhu · Chengzu Li · Tiancheng Hu · Yijiang River Dong · Deqing Yang · Nigel Collier

摘要

While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research overwhelmingly focuses on single-turn settings. The dynamics of model confidence in multi-turn conversations, where context accumulates and ambiguity is progressive...

智能体与工具安全可解释性OpenReview 项目页
作者位次 #8/8ACL 2026Findings· Findings of ACL

Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning

Tiehua Mei · Minxuan Lv · Leiyu Pan · Zhenpeng Su · Hongru Hou · Hengrui Chen · Ao Xu · Deqing Yang

摘要

Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance. We observe that better reasoning makes better demonstrations : high-quality s...

强化学习与后训练推理评测与基准OpenReview 项目页

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