Tzu-Han Lin

Incoming Ph.D. student @ UCLA

I am an incoming Ph.D. student in Computer Science at UCLA, where I am fortunate to be advised by Prof. Kai-Wei Chang.

Previously, I received my M.S. degree from the Graduate Institute of Networking and Multimedia at National Taiwan University, where I was advised by Prof. Yun-Nung (Vivian) Chen. I was also a visiting research intern at the University of Virginia, where I worked with Prof. Yu Meng.

I received my bachelor’s degree from National Taiwan University. During my undergraduate studies, I worked with Prof. Hung-yi Lee and Prof. Yun-Nung (Vivian) Chen on speech processing and natural language processing.

Research Focus: My research centers on natural language processing, particularly Large Language Models (LLMs). I am interested in developing LLMs and agents that are resource-efficient and reliable.

Nowadays, I think about:

Language Agents:
- Trustworthiness: Beyond task completion, how can we make the behavior of agents more interpretable and faithful to users?
- Efficiency: How can we control or reduce unnecessary actions in agents? How should we quantify and balance the trade-off between reasoning and action?
Self-Improving/Self-Evolving AI Systems:
- Conditions for Autonomous Improvement: What are the necessary conditions for an AI system to reliably and sustainably self-improve?
- Alignment: How can we ensure that self-improving systems remain aligned with human values as they surpass the level at which humans can easily provide direct supervision?
- Verification and Evaluation at Scale: How can we efficiently verify and evaluate progress on long-horizon, open-ended tasks that are inherently difficult to assess (e.g., scientific research agents)?

Misc: When I am not doing research, I like to listen to different genre of musics, especially Blues, Rock, and Grunge. I’m a huge fan of John Mayer and I love to play eletric guitars!

news

Aug 21, 2026	AdaSearch is accepted to EMNLP’26 main conference. See you in Budapest 🇭🇺!
Apr 08, 2026	I’m thrilled to announce that I will join UCLA as a Computer Science Ph.D. student in Fall 2026, where I will be advised by Prof. Kai-Wei Chang!
Dec 18, 2025	Our new preprint AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning is out! We investigate how LLM search agents should balance parametric knowledge and search, and propose AdaSearch, a framework that teaches agents to explicitly decide when to search via RL. It achieves superior self-knowledge awareness without complex reward engineering, and provides transparent decision-making rationales.
May 27, 2025	Excited to share that I have joined Prof. Yu Meng’s group as a visiting research intern, and I’m grateful for the opportunity to work with the team!
Feb 19, 2025	Our new preprint Transferring Textual Preferences to Vision-Language Understanding through Model Merging is out! We show that text scalar RMs can be merged into Vision LLMs to build VL-RMs. (Update 05/2025: The paper is accepted to ACL 2025 Main.)

selected publications

AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

Tzu-Han Lin, Wei-Lin Chen, Chen-An Li, Hung-yi Lee, Yun-Nung Chen, and Yu Meng

In The 2026 Conference on Empirical Methods in Natural Language Processing, 2026

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Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively balancing internal parametric knowledge with external search remains a challenge, as overreliance on search introduces unnecessary cost and risks exposure to noisy or malicious content, while relying solely on parametric knowledge risks hallucination. Prior efforts mitigate search overuse through tool-call reward shaping, which requires heavy reward engineering and conflates necessary and unnecessary search. To address these limitations, we revisit the evaluation of search agents through an F1-based decision metric, revealing that prior methods often overlook readily available parametric knowledge. Motivated by this, we propose AdaSearch, a simple two-stage, outcome-driven RL framework that disentangles problem-solving from the decision to search, making the decision process explicit and interpretable. Extensive experiments demonstrate that AdaSearch significantly improves search invocation decision-making and reduces unnecessary search calls while preserving strong task performance.
Transferring Textual Preferences to Vision-language Understanding Through Model Merging

Chen-An Li, Tzu-Han Lin, Yun-Nung Chen, and Hung-yi Lee

In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2025

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Large vision-language models (LVLMs) perform outstandingly across various multimodal tasks. However, their ability to evaluate generated content remains limited, and training vision-language reward models (VLRMs) with preference data is computationally expensive. This paper explores a training-free alternative by merging text-based reward models (RMs) with LVLMs to create VLRMs. Our approach shows that integrating these models leads to improved performance over LVLMs’ scoring and text-based RMs, offering an efficient method for incorporating textual preferences into LVLMs.
DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging

Tzu-Han Lin, Chen-An Li, Hung-Yi Lee, and Yun-Nung Chen

In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

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Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF. However, collecting paired preference data for training reward models is often costly and time-consuming, especially for domain-specific preferences requiring expert annotation. To address this challenge, we propose the **Do**main knowled**ge** merged **R**eward **M**odel (**DogeRM**), a novel framework that integrates domain-specific knowledge into a general reward model by model merging. The experiments demonstrate that DogeRM enhances performance across different benchmarks and provide a detailed analysis showcasing the effects of model merging, showing the great potential of facilitating model alignment."
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

Tzu-Han Lin^*, How-Shing Wang^*, Hao-Yung Weng^**, Kuang-Chen Peng^**, Zih-Ching Chen, and Hung-yi Lee

In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2024

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Parameter-Efficient Fine-Tuning (PEFT) is increasingly recognized as an effective method in speech processing. However, the optimal approach and the placement of PEFT methods remain inconclusive. Our study conducts extensive experiments to compare different PEFT methods and their layer-wise placement adapting Differentiable Architecture Search (DARTS). We also explore the use of ensemble learning to leverage diverse PEFT strategies. The results reveal that DARTS does not outperform the baseline approach, which involves inserting the same PEFT method into all layers of a Self-Supervised Learning (SSL) model. In contrast, an ensemble learning approach, particularly one employing majority voting, demonstrates superior performance. Our statistical evidence indicates that different PEFT methods learn in varied ways. This variation might explain why the synergistic integration of various PEFT methods through ensemble learning can harness their unique learning capabilities more effectively compared to individual layer-wise optimization.