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Ke Tang

ACL 2026×2NeurIPS 2026强化学习与后训练推理效率与系统对齐与偏好智能体与工具
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一作贡献0
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方向广度13
合作网络6
0 一作 · 3 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

3
作者位次 #5/5ACL 2026Findings· Findings of ACL

Towards Bridging the Reward-Generation Gap in Direct Alignment Algorithms

Zeguan Xiao · Yun Chen · Jian Yang · Guanhua Chen · Ke Tang

摘要

Direct Alignment Algorithms (DAAs), such as Direct Preference Optimization (DPO) and Simple Preference Optimization (SimPO), have emerged as efficient alternatives to Reinforcement Learning from Human Feedback (RLHF) algorithms for aligning large language models (LLMs) with human preferences. How...

对齐与偏好强化学习与后训练效率与系统OpenReview 项目页
作者位次 #2/2ACL 2026Findings· Findings of ACL

Why Agents Compromise Safety Under Pressure

Hengle Jiang · Ke Tang

摘要

Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant exec...

推理智能体与工具对齐与偏好OpenReview 项目页

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