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Peijie Wang

ACL 2026×2推理强化学习与后训练效率与系统多模态
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一作贡献30
核心/参与5
方向广度8
合作网络9
1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #5/14ACL 2026Poster· Natural Language Processing

Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

Zhong-Zhi Li · Xiao Liang · Zihao Tang · Lei Ji · Peijie Wang · Haotian Xu · Xing W · Haizhen Huang · Weiwei Deng · Yeyun Gong · Zhijiang Guo · Xiao Liu · Fei Yin · Cheng-lin Liu

摘要

Large Language Models (LLMs) have recently achieved remarkable progress on complex reasoning tasks by leveraging extended Chain-of-Thought (CoT) techniques. These reasoning processes can be roughly categorized into System-1 (fast and intuitive) and System-2 (slow and deliberate) paradigms. Howeve...

推理效率与系统强化学习与后训练OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language

Peijie Wang · Ming-Liang Zhang · Jun Cao · Chao Deng · Dekang Ran · Pi Bu · Hongda Sun · Xuan Zhang · Yingyao Wang · Jun Song · Zerui Huang (Taobao &amp · Fei Yin · Cheng-lin Liu

摘要

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have aided plane geometry understanding, solid geometry which req...

强化学习与后训练多模态推理OpenReview 项目页

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