Xin-Qiang Cai / Research Log
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Xin-Qiang Cai

Reinforcement Learning, Reward Modeling, and Weak Supervision. Postdoctoral Researcher, Imperfect Information Learning Team, RIKEN AIP (Tokyo).

$ role --current
postdoc / riken-aip
$ focus --areas
reward modeling · rl · weak supervision
$ location
tokyo, japan
Research Stack

Current Focus

Reward Modeling Reinforcement Learning Imitation Learning Weak Supervision RLHF Reliability Offline RL
Papers

Selected Publications & Preprints

  1. VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
  2. Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
  3. Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
  4. Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
  5. PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models
  6. UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimality
  7. Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains (RLC 2025)
  8. Imitation Learning from Vague Feedback (NeurIPS 2023)
  9. Distributional Pareto-Optimal Multi-Objective RL (NeurIPS 2023)
  10. Heterogeneously Observable Imitation Learning (ICLR 2023)

For the complete list, visit Publications.

Courses

Teaching

  • 2026 S1S2 (Graduate): Special Topics in Mechano-Informatics II at The University of Tokyo
  • 2026 Spring (Undergraduate/Graduate): Special Lecture on Information Science IV at Ochanomizu University

For the complete list, visit Teaching.

Timeline

Recent Updates

  • 📝 I was invited to serve as an Area Chair for ICLR 2027.
  • ✈️ I attended ICML 2026 in Seoul, South Korea.
  • 🎉 One paper was accepted by ICML 2026.
  • Lecture on "An Introduction to Reinforcement Learning" for the course "Special Lecture on Information Science IV" at Ochanomizu University.
  • Lecture on "An Introduction to Reinforcement Learning" for the course "Special Topics in Mechano-Informatics II" at The University of Tokyo.
  • 🎉 My research proposal titled "Reinforcement Learning with Unreliable Verification" has been selected for funding by the JSPS Grant-in-Aid for Early-Career Scientists (KAKENHI 若手研究).
  • VI-CuRL released on arXiv.
  • PU Reinforcement Learning Distillation released on arXiv.
  • Multi-Label Toxicity Evaluation released on arXiv.
  • Paper on cross-domain offline RL accepted by RLC 2025.
Community

Professional Service

Contact

Correspondence

For research collaboration, please contact via email above. Full publication list and PDFs are available through Publications and Google Scholar.