Research

My research aims at self-improving AI, organized as a stack in which every layer has to hold:

• Data & Environments. Interactive playgrounds where agents run the real experiment loop, and automated pipelines that manufacture verifiable tasks at scale (MLE-Dojo, MLE-Smith).
• Algorithms. Post-training that stays dense, reliable, and on-policy over long horizons (Agent DAgger).
• Infrastructure. Modular systems that schedule and scale agentic reinforcement learning (STACX).
• Harnesses. Hierarchical orchestration that sustains long-horizon optimization and research (Matryoshka Agent).

I wish to find the right way of scaling for Recursive Self-Improvement.

I strive for research and projects that are scalable and have real impact on frontier models.

I contribute to frontier models such as Gemini (3.5 Flash, 3.6 Flash).

MLE-Dojo and MLE-Smith have been widely adopted for building MLE/RSI agents and systems, and have proven scalable (e.g., OpenRSI).

Publications & Preprints

• ICLR 2026 MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline Rushi Qiang, Yuchen Zhuang, Anikait Singh, Percy Liang, Chao Zhang, Sherry Yang, Bo Dai
• NeurIPS 2025 · Datasets & Benchmarks MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering Rushi Qiang^*, Yuchen Zhuang^*, Yinghao Li, Dingu Sagar V K, Rongzhi Zhang, Changhao Li, Ian Shu-Hei Wong, Sherry Yang, Percy Liang, Chao Zhang, Bo Dai
• NeurIPS 2025 Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs Changhao Li^*, Yuchen Zhuang^*, Rushi Qiang, Haotian Sun, Hanjun Dai, Chao Zhang, Bo Dai
• COLM 2025 Language Model Uncertainty Quantification with Attention Chain Yinghao Li, Rushi Qiang, Lama Moukheiber, Chao Zhang
• NeurIPS 2024 HYDRA: Model Factorization Framework for Black-Box LLM Personalization Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, Bo Dai
• NAACL 2024 AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning Ruiyi Zhang^*, Rushi Qiang^*, Sai Ashish Somayajula, Pengtao Xie
• arXiv · 2026 Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering Rushi Qiang, Changhao Li, Haotian Sun, Yuchen Zhuang, Chao Zhang, Bo Dai
• arXiv · 2026 Revisiting DAgger in the Era of LLM-Agents Changhao Li, Rushi Qiang^†, Jiawei Huang^†, Chenxiao Gao^†, Chao Zhang, Niao He, Bo Dai
• arXiv · 2026 Forward-Free Diffusion Language Models Haotian Sun, Rushi Qiang, Yuqian Zheng, Bo Dai
• arXiv · 2025 Towards Better Instruction Following Retrieval Models Yuchen Zhuang^*, Aaron Trinh^*, Rushi Qiang^*, Haotian Sun, Chao Zhang, Hanjun Dai, Bo Dai

^* equal contribution  ·  ^† equal second authorship

Software

• STACX A modular infrastructure for end-to-end agentic reinforcement learning: a sandboxed task-execution engine coupled with a multi-algorithm training engine (GRPO, SFT, DAgger) for scheduling and scaling agent training.
• MLE-Dojo A Gym-style framework for training, evaluating, and improving LLM agents on iterative machine learning engineering workflows, with interactive sandbox environments built on 200+ real Kaggle competitions.

Experience

• Google DeepMind, Mountain View Student Researcher · agentic MLE for Gemini Feb 2026 – present

Education

• Georgia Institute of Technology, Atlanta Ph.D. student in Machine Learning · co-advised by Bo Dai and Chao Zhang Aug 2024 – present
• Tsinghua University, Beijing B.Eng. in Automation Sep 2020 – Jun 2024

Academic Service

Reviewer for NeurIPS, ICLR, ICML, and ACL Rolling Review.