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Yong Wang

ACL 2026×7ICLR 2026×6强化学习与后训练评测与基准多模态推理安全
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0 一作 · 13 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

13
作者位次 #4/6ICLR 2026Poster· Applications / Language, Speech and Dialog

Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulation

Yanqi Dai · Yuxiang Ji · Xiao Zhang · Yong Wang · Xiangxiang Chu · Zhiwu Lu

推理多模态强化学习与后训练OpenReview
作者位次 #5/8ACL 2026Poster· Natural Language Processing

Visually-Guided Policy Optimization for Multimodal Reasoning

Zengbin Wang · Feng Xiong · Liang Lin · Xuecai Hu · Yong Wang · Yanlin Wang · Man Zhang · Xiangxiang Chu

摘要

Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the inherent text-dominated nature of VLMs often leads to insufficient visual faithfulness, characterized by sparse attention activation to visual toke...

多模态强化学习与后训练推理OpenReview 项目页
作者位次 #3/7ACL 2026Poster· Natural Language Processing

Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem

Zeguan Xiao · Siqing Li · Yong Wang · Xuetao Wei · Jian Yang · Yun Chen · Guanhua Chen

摘要

Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM unlearning as an asymmetric two-task problem: retention is the primary objective and forgetting is an auxiliary. From this perspective, we propos...

对齐与偏好安全评测与基准OpenReview 项目页
作者位次 #2/7ACL 2026Poster· Natural Language Processing

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models

Shun Zou · Yong Wang · Zehui Chen · Lin Chen · Chongyang Tao · Feng Zhao · Xiangxiang Chu

摘要

Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive (Semi-AR) decoding is widely employed in base dLLMs and advanced decoding strategies due to its superior performance. However, our observations...

多模态效率与系统评测与基准OpenReview 项目页
作者位次 #5/6ACL 2026Poster· Natural Language Processing

C o E volve: Training LLM Agents via Agent-Data Mutual Evolution

Shidong Yang · Ziyu Ma · Tongwen Huang · Yiming Hu · Yong Wang · Xiangxiang Chu

摘要

Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent’s evolving behavior and leads to poor coverage of complex environment interactions. To address these challenges, we propose CoEvolve, an agent-data mutual evolution framew...

智能体与工具强化学习与后训练OpenReview 项目页
作者位次 #2/9ACL 2026Findings· Findings of ACL

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization

Yuxiang Ji · Yong Wang · Ziyu Ma · Yiming Hu · Hailang Huang · Xuecai Hu · Guanhua Chen · Liaoni Wu · Xiangxiang Chu

摘要

The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues.Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by h...

推理多模态强化学习与后训练OpenReview 项目页
作者位次 #6/8ACL 2026Findings· Findings of ACL

R eal S ec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories

Yanlin Wang · Ziyao Zhang · Chong Wang · Xinyi Xu · Mingwei Liu · Yong Wang · Jiachi Chen · Zibin Zheng

摘要

Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. Existing benchmarks often fall short by relying on synthetic vulnerabilities or evaluating functional correctness in i...

代码安全评测与基准OpenReview 项目页
作者位次 #5/11ACL 2026Findings· Findings of ACL

From Conversation to Evaluation: Benchmarking LLM s on Development Knowledge via S imple D ev QA

Jing Zhang · Lianghong Guo · Yanlin Wang · Terry Yue Zhuo · Yong Wang · Mingwei Liu · Jiachi Chen · Ensheng Shi · Yuchi Ma · Hongyu Zhang · Zibin Zheng

摘要

The Development Knowledge Question Answering (Dev Knowledge QA) task aims to provide accurate natural language answers to knowledge-seeking questions during software development. To investigate the importance of Dev Knowledge QA in AI-assisted software development and the extent to which it has b...

检索与知识评测与基准代码OpenReview 项目页

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