Zeyu Gan (甘泽宇)

Zeyu Gan (甘泽宇)

Renmin Univerisity of China

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Superclass Learning with Representation Enhancement

Published in CVPR, 2023

This paper introduced a novel coarse-grained classification situation called superclass learning, and proposed an attention-based framework (SCLRE) to extract superclass-aware representations.

Recommended citation: Zeyu Gan, Suyun Zhao, Jinlong Kang, Liyuan Shang, Hong Chen, Cuiping Li. Superclass Learning With Representation Enhancement. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 24060-24069, 2023. https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Superclass_Learning_With_Representation_Enhancement_CVPR_2023_paper.html

Towards a Theoretical Understanding of Semi-Supervised Learning Under Class Distribution Mismatch

Published in TPAMI, 2025

This paper theoretically analyzes the excess risk between the empirical optimal solution and the population-level optimal solution for semi-supervised learning under class distribution mismatch.

Recommended citation: Pan Du, Suyun Zhao, Puhui Tan, Zisen Sheng, Zeyu Gan, Hong Chen, Cuiping Li. Towards a Theoretical Understanding of Semi-Supervised Learning Under Class Distribution Mismatch. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 47, 6 (June 2025), 4853-4868. https://dl.acm.org/doi/10.1109/TPAMI.2025.3545930

Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective

Published in ICLR, 2025

This paper explores the critical role of synthetic data in enhancing the post-training performance of large language models (LLMs) from a novel reverse-bottleneck perspective.

Recommended citation: Zeyu Gan, Yong Liu. Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. In The Thirteenth International Conference on Learning Representations (ICLR), 2025. https://arxiv.org/abs/2410.01720

Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning

Published in ICML, 2025

This paper theoretically analyzes external slow-thinking methods in LLMs, linking snowball errors to reasoning accuracy and providing insights to enhance the interpretability of existing approaches.

Recommended citation: Zeyu Gan, Yun Liao, Yong Liu. Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning. In The 42nd International Conference on Machine Learning (ICML), 2025. https://arxiv.org/abs/2501.15602

CoT-Space: A Theoretical Framework for Internal Slow-Thinking via Reinforcement Learning

Published in ArXiv, 2025

This paper introduces CoT-Space, a novel theoretical framework that recasts LLM reasoning as a continuous optimization problem, which provides a coherent explanation for empirical phenomena such as overthinking.

Recommended citation: Zeyu Gan, Hao Yi, Yong Liu. CoT-Space: A Theoretical Framework for Internal Slow-Thinking via Reinforcement Learning. arXiv preprint arXiv:2509.04027, 2025. https://arxiv.org/abs/2509.04027

Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn LLM Agents

Published in ArXiv, 2025

This paper proposes Information Gain-based Policy Optimization (IGPO), a simple yet effective RL framework that provides dense and intrinsic supervision for multi-turn agent training.

Recommended citation: Guoqing Wang, Sunhao Dai, Guangze Ye, Zeyu Gan, Wei Yao, Yong Deng, Xiaofeng Wu, Zhenzhe Ying. Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn LLM Agents. arXiv preprint arXiv:2510.14967, 2025. https://arxiv.org/abs/2510.14967

Beyond the Black Box: Theory and Mechanism of Large Language Models

Published in ArXiv, 2026

This survey paper provides a structured roadmap for transitioning LLM development from engineering heuristics toward a principled scientific discipline.

Recommended citation: Zeyu Gan, Ruifeng Ren, Wei Yao, Xiaolin Hu, Gengze Xu, Chen Qian, Huayi Tang, Zixuan Gong, Xinhao Yao, Pengwei Tang, Zhenxing Dou, Yong Liu. Beyond the Black Box: Theory and Mechanism of Large Language Models. arXiv preprint arXiv:2601.02907, 2026. https://arxiv.org/abs/2601.02907

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.