Yuxuan Jiang

Ph.D. Student ⎟ LLM Post-Training & Reasoning ⎟ Recursive Self-Improvement

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Yuxuan Jiang

yuxuanj1@umbc.edu


Affiliations

University of Maryland, Baltimore County
Ph.D. in Computer Science
2023 - Present (expected May 2027)
New York University
MS. in Computer Science
2021-2022
Advisor: Dr. He He
Grade: 3.6/4.0
Beijing Language and Culture University
B.Sc. in Information Systems
Fall 2016 - Fall 2020

Professional Services

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

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.

  1. Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification
    Yuxuan Jiang, Aditya Vempaty, and Ashish Jagmohan
    arXiv preprint, 2026
  2. Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
    Yuxuan Jiang, and Francis Ferraro
    arXiv preprint, 2026
  3. NeurIPS
    Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
    Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta, Dawei Li, and Zhao Yang
    NeurIPS 2026, 2026
  4. EMNLP
    SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
    Yuxuan Jiang, and Francis Ferraro
    EMNLP 2026 Main, 2026
  5. DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models
    Yuxuan Jiang, Dawei Li, and Francis Ferraro
    ACL 2026 Findings, 2026
  6. EACL
    Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs
    Yuxuan Jiang, and Francis Ferraro
    EACL 2026 (Oral), 2026