[2024.09] 🎉 Our work CurrMask is accepted to NeurIPS 2024! See you in Vancouver! 🎉

[2025.02] QLASS is on arXiv!;

[2025.05] 🎉 QLASS is accepted to ICML 2025! See you in Vancouver again! 🎉

I am interested in building generalized AI agents under limited supervision. Take my dream of developing a 🧋Boba-Agent that can make all kinds of boba as an example.

🤔To train a Boba-Agent, the most straightforward way is to collect a bunch of boba expert demos and train the agent via imitation learning. But data collection is too costly and the model can poorly generalize to making 🥭mango pomelo sago if the data is on 🍋lemon tea.

🤔Another choice is to allow the Boba-Agent learning from a large amount of trial-and-errors. The agent can keep exploring the kitchen, making different boba, receiving my reward, and improving its boba-making policy via reinforcement learning. Unfortunately, this process is friendly for neither me nor the kitchen.

😀The ideal case is that the agent can firstly search online and learn from offline video demos and textual instructions, extract reusable knowledge, and learn to safely navigate the kitchen, and quickly adapt to personal preferences. (For instance, learning that my highest praise is reserved for boba that's "not too sweet"!)

To summarize, my interests lie in how to enable effective internal world modeling to help the agent learn from large-scale boba-making offline data and extract reusable knowledge, as well as external world interaction, e.g., searching for new recipes online, exploring the kitchen, and getting feedback from me, so that it can quickly make delicious newly invented Boba under limited supervision.

My research experiences mainly lie in the intersection of reinforcement learning, decision making and natural language processing. Specifically, I have been working on projects including:

I enjoy Jazz Dance and am a beginner at guitar.

I also love to play with my puppy Happy through Reinforcement Learning. Here is a video of our progress when she was 4 months old.