Ting-Han Fan
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I am an AI Scientist on the CodeGen team at Mistral AI. Previously, I was a Member of Technical Staff at xAI, working on code pre-training, and a Research Scientist at ByteDance Seed, focusing on LLM function calling across mid-training and post-training. Before that, I was a Machine Learning Engineer at TikTok E-commerce Recommendation, a Summer Associate at Goldman Sachs, and a Research Intern at Siemens and Microsoft. I completed my M.A. and Ph.D. in Electrical and Computer Engineering at Princeton University, where I was very fortunate to be advised by Prof. Peter J. Ramadge. Prior to Princeton, I received my B.S. in Electrical Engineering from National Taiwan University. email: tinghanfan at gmail dot com Find me at Google Scholar and LinkedIn. |
Publications:
At ByteDance Seed
- Seed1.8 Model Card: Towards Generalized Real-World Agency, ByteDance Seed, arXiv:2603.20633.
- Virtual Width Networks, ByteDance Seed, arXiv:2511.11238.
- Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning, ByteDance Seed, arXiv:2504.13914.
- MIR-Bench: Can Your LLM Recognize Complicated Patterns via Many-Shot In-Context Reasoning?, Kai Yan, Zhan Ling, Kang Liu, Yifan Yang, Ting-Han Fan, Lingfeng Shen, Zhengyin Du, Jiecao Chen, NeurIPS 2025 Datasets and Benchmarks Track.
- LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion, Zhan Ling, Kang Liu, Kai Yan, Yifan Yang, Weijian Lin, Ting-Han Fan, Lingfeng Shen, Zhengyin Du, Jiecao Chen, arXiv:2501.15089.
Length Extrapolation of Transformers
- Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation, Ta-Chung Chi, Ting-Han Fan, Alexander I. Rudnicky, Findings of NAACL 2024.
- Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis, Ta-Chung Chi, Ting-Han Fan, Alexander I. Rudnicky, Peter J. Ramadge, ACL 2023. (Outstanding Paper Award) [link]
- Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings, Ta-Chung Chi, Ting-Han Fan, Li-Wei Chen, Alexander I. Rudnicky, Peter J. Ramadge, ACL 2023. (Honorable Mention) [link]
- KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation, Ta-Chung Chi*, Ting-Han Fan*, Peter J. Ramadge, Alexander I. Rudnicky, NeurIPS 2022. [code]
Regular Language Reasoning
- Advancing Regular Language Reasoning in Linear Recurrent Neural Networks, Ting-Han Fan*, Ta-Chung Chi*, Alexander I. Rudnicky, NAACL 2024. [code]
- Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation, Ta-Chung Chi, Ting-Han Fan, Alexander I. Rudnicky, Peter J. Ramadge, Findings of EMNLP 2023.
Reparameterization for Discrete Deep Generative Models
- Training Discrete Deep Generative Models via Gapped Straight-Through Estimator, Ting-Han Fan*, Ta-Chung Chi*, Alexander I. Rudnicky, Peter J. Ramadge, ICML 2022. [code] [SlidesLive]
Reinforcement Learning for Power Distribution System Controls
- PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems, Ting-Han Fan, Xian Yeow Lee, Yubo Wang, L4DC 2022. [code]
- Soft Actor-critic With Integer Actions, Ting-Han Fan, Yubo Wang, ACC 2022.
Model-based Reinforcement Learning
- A Contraction Approach to Model-based Reinforcement Learning, Ting-Han Fan, Peter J. Ramadge, AISTATS 2021. [SildesLive]
(* denotes equal contribution)
Patent:
- System and method for supporting execution of batch production using reinforcement learning, Ting-Han Fan, Yubo Wang, Ulrich Muenz, Mathias Hakenberg, US Patent 12,579,587
- System and method for controlling large scale power distribution systems using reinforcement learning, Ting-Han Fan, Yubo Wang, Ulrich Muenz, US Patent App. 17/814535.
Academic Activities:
Reviewer
- NeurIPS 2022-2025, ICLR 2024-2026, ICML 2022-2025, ISIT 2024, L4DC 2023, AISTATS 2021 & 2024
- ACL Rolling Review: 2023 December - 2024 December
Teaching Assistant at Princeton University
- ECE 435/535: Machine Learning and Pattern Recognition, Fall 2019, Fall 2020, Fall 2022 (head TA)
- EGR 154: Foundations of Engineering: Linear Systems, Spring 2022
- COS 302: Mathematics for Numerical Computing and Machine Learning, Fall 2021 (head TA)
- SML 310: Research Projects in Data Science, Spring 2021 (head TA)
- ECE 201: Information Signals, Spring 2020, Spring 2023