Haowei Lin

E-mail: linhaowei (at) pku (dot) edu (dot) cn

I am Haowei Lin, a third year Ph.D. student at the Institute for Artificial Intelligence, Peking University, co-advised by Prof. Yitao Liang, Prof. Di He, and Prof. Jianzhu Ma.

I received my Bachelor’s degree in Artificial Intelligence from Yuanpei College, Peking University, where I was fortunate to work with Prof. Bing Liu and Dr. Zixuan Ke on OOD detection, continual learning, and language models. We are the first to propose the task of continual pre-training for LLMs (EMNLP22, ICLR23), and the first to apply OOD detection methods to continual learning (EMNLP23, ICLR24).

I work on unified cross-modal generative foundation models (GFMs) for scientific discovery. My current research focuses on:

Foundations and unification of GFMs. Creating useful experiences that apply to both diffusion models (including flow-matching) and autoregressive architectures, and studying their scaling laws and training-free guidance (TFG).
Cross-modal generative modeling. Building models that operate across language, video, MDPs, and 3D/molecular data.
AI for scientific discovery. Applying and post-training GFMs for complex reasoning, open-world gaming, and discovering new laws in space physics and neural network scaling.

Outside of my professional interests, I enjoy engaging in music-related activities, including singing and playing the guitar.

If you’re interested in working with me on GFMs / AI Scientist, please contact me through e-mail.

news

Dec 07, 2025	Glad to launch a new blog on Scaling Law Discovery (SLD) with OpenEvolve, in collaboration with Algorithmic SuperIntelligence Labs. We hope our work on SLD helps advance foundation model development and push the boundaries of AI Scientist. Code, dataset, benchmarks, and leaderboard are all publicly available.
Oct 21, 2025	Excited to be a core contributor of adapters in Terminal-Bench, which converts all agentic benchmarks (e.g., SWE-related) in a unified format to t-bench! Happy to see OAI, GDM, Anthropic, DeepSeek, etc. using T-Bench for model evaluation in their model release.
Oct 20, 2025	Our paper on AI for scientific discovery was published in Nature Machine Intelligence as a cover paper!

selected publications

Nat. Mach. Intell.

A Neural Symbolic Model for Space Physics

Jie Ying^*, Haowei Lin^*, Chao Yue^*, and 7 more authors

Nature Machine Intelligence (Cover Paper),

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Using large-scale synthetic data to train autoregressive models for symbolic regression in space physics.
ICML Spotlight

MCU: An Evaluation Framework for Open-Ended Game Agents

Xinyue Zheng^*, Haowei Lin^*, Kaichen He, and 5 more authors

In The Forty-second International Conference on Machine Learning (ICML 2025),

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The first evaluation framework for open-ended game agents, with a task generator to synthesize infinite open-ended tasks and VLM-based evaluator.
ICLR

TFG-Flow: Training-free Guidance in Multimodal Generative Flow

Haowei Lin^*, Shanda Li^*, Haotian Ye, and 4 more authors

In The Thirteenth International Conference on Learning Representations (ICLR 2025),

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Adapting training-free guidance method from continuous diffusion to multimodal flow matching (discrete + continuous).
NeurIPS Spotlight

TFG: Unified Training-Free Guidance for Diffusion Models

Haotian Ye^*, Haowei Lin^*, Jiaqi Han^*, and 6 more authors

In Advances in Neural Information Processing Systems 37 (NeurIPS 2024),

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The first unified framework for training-free guidance in diffusion models and comprehensive benchmarks on image, audio, molecules.
ICML

Selecting Large Language Model to Fine-tune via Rectified Scaling Law

Haowei Lin^*, Baizhou Huang^*, Haotian Ye^*, and 7 more authors

In The Forty-first International Conference on Machine Learning (ICML 2024),

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The first paper to introduce the problem of LLM selection. Proposed to use scaling law for LLM selection. Rectified vanilla power law in LLM fine-tuning scaling.
ICLR

Class Incremental Learning via Likelihood Ratio-Based Task Prediction

Haowei Lin, Yijia Shao, Weinan Qian, and 3 more authors

In The Twelfth International Conference on Learning Representations (ICLR 2024),

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A theory-guided class incremental learning method, being state-of-the-art for more than 3 years (almost reaching performance upper bound).
ICLR

Continual Pre-Training of Language Models

Zixuan Ke^*, Yijia Shao^*, Haowei Lin^*, and 3 more authors

In The Eleventh International Conference on Learning Representations (ICLR 2023),

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The first paper to introduce the concept of continual pre-training (CPT), with comprehensive benchmarks and a new method motivated by model sparsity.