Papers by Yikun Wang
MARS2: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation (2026.acl-long)
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Pengfei Li, Shijie Wang, Fangyuan Li, Yikun Fu, Kaifeng Liu, Kaiyan Zhang, Dazhi Zhang, Yuqiang Li, Biqing Qi, Bowen Zhou
| Challenge: | Existing approaches to reinforcement learning are decoupled from structured search due to limited trajectory diversity. |
| Approach: | They propose a unified RL framework that integrates multiple agents within a shared tree-structured search environment. |
| Outcome: | Experiments show that MARS2 improves performance across diverse model combinations and training settings. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)
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| Challenge: | Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally. |
| Approach: | They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples. |
| Outcome: | The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks. |
VideoPro: Adaptive Program Reasoning for Long Video Understanding (2026.acl-long)
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Chenglin Li, Feng Han, Yikun Wang, Ruilin Li, Shuai Dong, Haowen Hou, Haitao Li, Qianglong Chen, Feng Tao, Jingqi Tong, Yin Zhang, Jiaqi Wang
| Challenge: | Existing methods for understanding long videos are limited due to the sparsity of visual evidence relevant to a given query. |
| Approach: | They propose a framework that enables VideoLLMs to reason over long videos and refine their predictions through executable programs. |
| Outcome: | The proposed framework outperforms existing methods across long-video understanding benchmarks. |
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (2024.acl-srw)
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| Challenge: | Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. Obtaining a large volume of expert-annotated data is costly for most tasks. |
| Approach: | They propose a method that trains the model to prioritize the best responses from a pool of candidates created for a task using ranking metrics. |
| Outcome: | The proposed method is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. |
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better (2026.findings-acl)
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| Challenge: | Typical large vision-language models emphasize vision-to-language alignment while overlooking fine-grained visual information. |
| Approach: | They introduce autoregressive semantic visual reconstruction (ASVR) that enables joint learning of visual and textual modalities within a unified autoregression framework. |
| Outcome: | The proposed model improves baselines and multimodal understanding benchmarks by 2-3%. |
GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization (2026.findings-acl)
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| Challenge: | Existing methods for auxiliary construction training are expensive and underperform . Existing Corresponding Author training methods lack self-correction capabilities in reasoning chains. |
| Approach: | They propose a reinforcement learning framework that rewards auxiliary construction with geometric reasoning by grouping construction rewards with a Length Reward. |
| Outcome: | Experiments on Geometry3K and MathVista show that GeometryZero outperforms baselines on auxiliary constructions. |
VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search (2025.acl-long)
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| Challenge: | Existing approaches to large vision-language models fail to capture interleaved nature of human visual-verbal reasoning processes. |
| Approach: | They propose a framework that integrates visuospatial and linguistic domains to facilitate multimodal slow thinking by enabling progressive visual-textual reasoning. |
| Outcome: | Experiments show that VisuoThink significantly improves reasoning capabilities even without fine-tuning. |