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Curriculum vitae. For contact, please use the social links on the homepage.

Contact Information

Name Daniil Tiapkin
Professional Title Research Scientist, Google DeepMind
Website https://d-tiapkin.github.io

Professional Summary

Research Scientist at Google DeepMind in Paris, working on foundation-model post-training.

I received my PhD in Applied Mathematics & Computer Science from École Polytechnique and Université Paris-Saclay (2025), advised by Éric Moulines and Gilles Stoltz.

My research interests include reinforcement learning, post-training of foundation models, and the connections between amortized sampling and RL.

Experience

  • 2026 -

    Paris, France

    Research Scientist
    Google DeepMind
    Research on post-training of foundation models.
  • 2024 - 2024

    Paris, France

    Student Researcher
    Google DeepMind
    Research on language-model distillation, supervised by Mathieu Blondel.
    • On Teacher Hacking in Language Model Distillation — ICML 2025.
  • 2023 - 2026

    Paris, France

    PhD Candidate
    École Polytechnique (CMAP) — Institut Polytechnique de Paris & LMO, Université Paris-Saclay
    Advised by Éric Moulines and Gilles Stoltz. Thesis: Sample-Efficient Reinforcement Learning: Exploration, Imitation, and Online Learning.

Education

Awards

  • 2024
    Oral presentation, AISTATS 2024
    AISTATS

    Generative Flow Networks as Entropy-Regularized RL selected for oral presentation.

  • 2022
    Oral presentation, ICML 2022
    ICML

    From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses selected for oral presentation.

Selected Publications

For the full and up-to-date list, see the publications page.

Skills

Research areas: Reinforcement learning, Foundation-model post-training (RLHF, distillation), Online learning and bandits, Sampling and Bayesian methods
Programming: Python, JAX, PyTorch, NumPy, C++

Languages

English : C1
French : B1
Russian : Native

Projects