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Jianshu Zhang

ACL 2026×2ICLR 2026推理强化学习与后训练效率与系统数据与预训练可解释性
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0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #3/5ACL 2026Poster· Natural Language Processing

E quiv P runer: Boosting Efficiency and Quality in LLM -Based Search via Action Pruning

Jiawei Liu · Qisi Chen · Jianshu Zhang · Quan Liu · Defu Lian

摘要

Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalenc...

推理效率与系统数据与预训练OpenReview 项目页
作者位次 #4/6ACL 2026Findings· Findings of ACL

Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning

Fei Wu · Zhenrong Zhang · Qikai Chang · Jianshu Zhang · Quan Liu · Jun Du

摘要

Reinforcement Learning with Verifiable Rewards (RLVR) elicits long chain-of-thought reasoning in large language models (LLMs), but outcome-based rewards lead to coarse-grained advantage estimation. While existing approaches improve RLVR via token-level entropy or sequence-level length control, th...

强化学习与后训练推理可解释性OpenReview 项目页

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