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Jian Cheng

ICML 2026×10MICRO 2026×2AAAI 2026ACL 2026CVPR 2026IROS 2026KDD 2026强化学习与后训练效率与系统多模态推理对齐与偏好
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0 一作 · 17 篇 · ICML 2026 / MICRO 2026 / AAAI 2026 / ACL 2026 / CVPR 2026 / IROS 2026 / KDD 2026

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2026 顶会论文

17
作者位次 #4/4ICML 2026Poster· Optimization / Discrete and Combinatorial Optimization

Preference-Modulated Structural Attention for Multi-Objective Combinatorial Optimization

Rongsheng Jia · Jun Zhang · Yifan Zhang · Jian Cheng

对齐与偏好OpenReview
作者位次 #3/3ICML 2026Poster· Reinforcement Learning / Deep RL

Mean Flow Policy Optimization

Xiaoyi Dong · Xi Zhang · Jian Cheng

强化学习与后训练OpenReview
作者位次 #3/3ICML 2026Poster· Optimization / Discrete and Combinatorial Optimization

Optimal Transport–Guided Stochastic Control for Graph Combinatorial Optimization

yang huang · Yifan Zhang · Jian Cheng

作者位次 #5/5ICML 2026Poster· Optimization / Discrete and Combinatorial Optimization

Game-Theoretic Co-Evolution for LLM-Based Heuristic Discovery

Xinyi Ke · Kai Li · Junliang Xing · Yifan Zhang · Jian Cheng

理论与学习动力学OpenReview
作者位次 #10/10ACL 2026Findings· Findings of ACL

FARSS : Fisher-Optimized Adaptive Low-Rank and Singular-Vector Selection for Knowledge-Preserving Fine-Tuning

Renxing Chen · Ziwei Xiang · Peisong Wang · Hongjian Fang · Meng Li · Fanhu Zeng · Yanan Zhu · Peipei Yang · Xu-yao Zhang · Jian Cheng

摘要

Parameter-efficient fine-tuning (PEFT) has become a prevalent approach for adapting large language models (LLMs). However, low-rank adaptation methods face an inherent trade-off: improving target task performance can compromise pre-trained world knowledge, while aggressively constraining updates...

效率与系统强化学习与后训练评测与基准OpenReview 项目页

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