Yuxuan Jiang
Ph.D. Student ⎟ LLM Post-Training & Reasoning ⎟ Recursive Self-Improvement
Yuxuan Jiang
yuxuanj1@umbc.edu
Affiliations
University of Maryland, Baltimore County
Ph.D. in Computer Science
2023 - Present (expected May 2027)
Advisor: Dr. Frank Ferraro
Beijing Language and Culture University
B.Sc. in Information Systems
Fall 2016 - Fall 2020
Professional Services
Reviewer of MATH-AI Workshop at NeurIPS 2025
I am a final-year Computer Science Ph.D. student at the University of Maryland, Baltimore County (UMBC), advised by Prof. Francis Ferraro, with expected graduation in May 2027. My research focuses on LLM post-training, reasoning, and recursive self-improvement.
I study how models learn from rewards, teacher guidance, and execution feedback. My work includes reusable process rewards for reasoning and tool use (SCRIBE), trajectory-aware on-policy distillation (TOPD), and understanding persistent teacher–student mismatches (Rock Tokens). I also study efficient reasoning through skill-aware step pruning.
During my research internship at Emergence AI (May–August 2026), I developed SO-RSI, a framework that investigates recurring failures before changing an agent’s workflow. On Lean 4 theorem proving and Verus code generation, it improved held-out pass rates over Naive RSI by 21.8 and 25.8 percentage points, respectively, under matched 24-hour optimization budgets.
My research combines method development and controlled experiments with hands-on training systems using PyTorch, FSDP2, SGLang, and Ray. My first-author work includes publications at NeurIPS 2026, EMNLP 2026, ACL 2026, and EACL 2026 (Oral).
Previously, I earned an M.S. in Computer Science from New York University and a B.S. in Information Systems from Beijing Language and Culture University.
I am seeking full-time AI research opportunities starting after my expected graduation in May 2027 and welcome research collaborations in LLM post-training, reasoning, and self-improving agents. Please email me to connect.
Research Interests
- Post-training and reasoning: reinforcement learning, process rewards, on-policy distillation, and reasoning efficiency.
- Self-improving agents: evidence-guided workflow optimization, failure diagnosis, and research automation.
- Evaluation and verification: tool use, formal verification, and reliable measurements of model behavior.
Recent News (See All)
| Oct 5, 2026 | Our preprint “Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification” is now available. SO-RSI uses diagnostic investigations to guide agent workflow improvements on Lean 4 and Verus. |
|---|---|
| Sep 24, 2026 | Our paper “Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation” has been accepted to NeurIPS 2026! |
| Aug 25, 2026 | Our paper “SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models” got accepted to EMNLP 2026 Main! |
| Apr 6, 2026 | Two papers got accepted in ACL 2026! |
| Jan 4, 2026 | Our paper “Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs” got accepted in EACL 2026 Main (Oral). |
Featured Publications
Check out Google Scholar for a full list of my publications.
- EACLBeyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMsEACL 2026 (Oral), 2026