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WJ

Wenhao Jiang

ACL 2026×3ICLR 2026强化学习与后训练效率与系统可解释性多模态多语言与语言学
新星分资深
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方向广度13
合作网络6
0 一作 · 4 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

4
作者位次 #4/5ICLR 2026Poster

Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learning

Hao Yu · Shuning Jia · Guanghao Li · Wenhao Jiang · Chun Yuan

多模态强化学习与后训练多语言与语言学OpenReview
作者位次 #10/10ACL 2026Poster· Natural Language Processing

R e E x- SQL : Reasoning with Execution-Aware Reinforcement Learning for Text-to- SQL

Yaxun Dai · Wenxuan Xie · Xialie Zhuang · Tianyu Yang · Ziyi Liu · Haiqin Yang · Yiying Yang · Yuhang Zhao · Pingfu Chao · Wenhao Jiang

摘要

Current Text-to-SQL reasoning models often lack integrated execution feedback during generation, and most existing approaches utilize feedback only for post-hoc correction. This separation not only limits real-time error correction, but may also introduce mistakes by altering otherwise correct SQ...

效率与系统代码推理OpenReview 项目页
作者位次 #3/3ACL 2026Poster· Natural Language Processing

SDE - SQL : Enhancing Text-to- SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

Wenxuan Xie · Yaxun Dai · Wenhao Jiang

摘要

Recent advances in large language models (LLMs) have led to substantial progress on the Text-to-SQL task. However, existing approaches typically depend on static, pre-processed database information supplied at inference time, which restricts the model’s capacity to deeply comprehend the underlyin...

评测与基准可解释性强化学习与后训练OpenReview 项目页
作者位次 #2/5ACL 2026Findings· Findings of ACL

Efficient Transformer Parameter Reuse via Zero-Token Mechanism

Guanghao Li · Wenhao Jiang · Li Shen · Ming Tang · Chun Yuan

摘要

Resource constraints often limit the parameter capacity of Large Language Models (LLMs), thereby hindering their performance. Although existing approaches leverage parameter sharing to reuse a fixed set of parameters within constrained budgets, they typically require each layer to fulfill multipl...

可解释性强化学习与后训练效率与系统OpenReview 项目页

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