Columbia University
Xuzhong Wang
Hi! I am an undergrad at Columbia University stuyding Computer Science. I am particularly interested in machine learning research, specifically in post-training LLMs and agents.
- Interests
- Reinforcement Learning
- Large Language Models
- Agent Design
- Graph Theory
Research
Broadly, I am interested in how agents can learn and adapt in complex environments. In parallel, I study and evaluate the representations in LLMs that emerge post-training—focusing on how models encode, interpret, and ground knowledge about the physical and conceptual world. Here are a few more concrete directions I am currently exploring:
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Multi-Objective Alignment
Develope techniques to improve how models navigate through distinct objectives — accuracy, cost, and format — in reasoning, coding, and tool usage.
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Unlearning
Develop techniques to erase the impact of certain data for ML models during training. Probe the LLM to see how "forget" acutally works in comparison to "confusion".
Selected Publications & Preprints
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EMNLP 2026 Findings Multi-Objective RL
Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning
It always takes longer than you expect, even when you take into account Hofstadter's Law.
— Hofstadter's Law
Industry Experience
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AnytimeAI
ML engineer internBuilt LLM agent pipelines for legal document analysis. An agent routing tool cut inference costs ~60%.
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Bestlink Technology
ML engineer internBuilt an OpenCV data retrieval pipeline and a LangChain-based QA agent for consulting clients.
Projects
MO-IKE
Python · PyTorchOfficial implementation of our multi-objective in-context knowledge editing work. An RL-trained retriever balances edit reliability, generality, and specificity against one another.
Akita
Go · SQLiteAn open-source computer architecture simulator I help maintain. I contributed the data recording package under Prof. Yifan Sun, for tracking simulation data across experiments.
Graph Recoloring
React · ViteAn interactive playground for graph coloring reconfiguration.