Yite Wang

Bellevue, WA

I’m a Senior Research Scientist at Snowflake AI Research, where I train LLM agents for software engineering, data science, and deep research. Before that, I was a Research Scientist on ByteDance’s Seed-Foundation-Code team.

I earned my Ph.D. from the University of Illinois at Urbana-Champaign under Prof. Ruoyu Sun, with co-advising from Prof. Naira Hovakimyan. In my first year I collaborated with Prof. Justin Sirignano on deep-learning for computational fluid dynamics (CFD), and during my master’s studies I worked with Prof. Kyle Smith on numerical simulation for energy storage systems.

Research interests: Reasoning Models; SWE Agents; Data Science Agents; Efficient Deep Learning; Natural Language Processing; Computer Vision; Numerical Methods.

news

Oct 23, 2026	Arctic-SWE, a project I co-led on asynchronous reinforcement learning and data curation for software-engineering agents, is now on arXiv, with an accompanying Snowflake engineering blog. [arXiv] [blog]
Sep 08, 2026	Our paper Benchmarking Hybrid Deep Research Across Database Querying and Web Search is now on arXiv. I co-led this project on evaluating research agents that combine SQL queries and web search. [arXiv] [blog] [code]
Jul 09, 2026	We have three papers accepted to COLM 2026. Papers coming soon!
Apr 30, 2026	Our paper Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning is accepted to ICML 2026. [arXiv] [code]
Feb 27, 2026	Our paper DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science is accepted to ICLR 2026. [arXiv] [code]

blogs

Arctic-SWE: The Missing Baseline for Agentic RL Model-system co-design and data curation for training software-engineering agents with asynchronous reinforcement learning.
Arctic RL: Open-Source Backend Open-source infrastructure for training and evaluating reinforcement-learning agents.
Hybrid Deep Research Benchmark A benchmark for evaluating long-horizon research agents across web and enterprise data.
ArcticSwarm: Hybrid Deep Research A hybrid agent system for deep research over private and public information sources.
ArcticSwarm: Multi-Agent System Architecture Architecture notes for coordinating specialized agents in enterprise research workflows.
Enterprise Text-to-SQL with Arctic R2 Reasoning models for production-grade text-to-SQL over enterprise data.
DARE-bench: LLM Data Science Workflows A benchmark for measuring modeling and instruction fidelity in data science workflows.
Agent World Model for Agentic Reinforcement Learning Synthetic environments for training agentic reinforcement-learning systems.

selected publications

* denotes equal contribution. ^† denotes project lead.

Arctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design

Boyi Liu^*^†, Yite Wang^*^†, Xinyu Lian^*, Fan Shu^*, Sida Li, Feng Yan, Minjia Zhang, Yuxiong He, and Zhewei Yao^†

Preprint, 2026

RL Data CurationSWE Agent TrainingModel-System Co-Design

HTML PDF Blog
Benchmarking Hybrid Deep Research Across Database Querying and Web Search

Ruofan Wu^*, Peiran Xu^*, Xiaolong Li^*, Fan Shu^*, Soyoung Yoon, Yite Wang^†, Xiaodong Yu^†, Boyi Liu, Feng Yan, Debiao Li, Yuxiong He, and Zhewei Yao

Preprint, 2026

Deep ResearchSQLEvaluation

HTML Code Blog Website Dataset
Bridging Databases and Documents: Data-Algorithm Co-Design for Hybrid Question Answering

Ruofan Wu, Boyi Liu, Fan Shu, Yite Wang, Zhewei Yao, Yuxiong He, and Feng Yan

In COLM, 2026

RL DataAgent RL TrainingBenchmark

HTML
MidTool: Mid-training Data Synthesis for Agentic Tool Use

Fengqing Jiang, Yite Wang^†, Boyi Liu, Zhaoyang Wang, Canwen Xu, Zhewei Yao, Radha Poovendran, and Yuxiong He

In COLM, 2026

Midtrain DataTool Use AgentAgent Training

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The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows

Junbo Li, Boyi Liu, Canwen Xu, Yite Wang, Yuxiong He, Zhangyang Wang, Qiang Liu, and Zhewei Yao

In COLM, 2026

Auto Research

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Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Zhaoyang Wang, Canwen Xu, Boyi Liu, Yite Wang, Siwei Han, Zhewei Yao, Huaxiu Yao, and Yuxiong He

In ICML, 2026

RL DataAgent RL Training

HTML Code Blog
DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science

Fan Shu^*, Yite Wang^*^†, Ruofan Wu, Boyi Liu, Zhewei Yao, Yuxiong He, and Feng Yan

In ICLR, 2026

RL DataAgent RL TrainingBenchmark

HTML Code Blog
FullStack Bench: Evaluating LLMs as Full-Stack Coders

ByteDance Seed-Foundation-Code Team

Preprint, 2025

Evaluation

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LEMON: Lossless Model Expansion

Yite Wang, Jiahao Su, Hanlin Lu, Cong Xie, Tianyi Liu, Jianbo Yuan, Haibin Lin, Ruoyu Sun, and Hongxia Yang

In ICLR, 2024

Model Expansion

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Balanced Training for Sparse GANs

Yite Wang^*, Jing Wu^*, Naira Hovakimyan, and Ruoyu Sun

In NeurIPS, 2023

Pruning

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NTK-SAP: Improving Neural Network Pruning by Aligning Training Dynamics

Yite Wang, Dawei Li, and Ruoyu Sun

In ICLR, 2023

Pruning

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Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

Haoxiang Wang^*, Yite Wang^*, Ruoyu Sun, and Bo Li

In CVPR, 2022

Neural Architecture SearchNTK

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