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Yiding Jiang
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Yiding Jiang

@yidingjiang
Research @GoogleDeepMind | Prev: PhD @mldcmu, AI resident @GoogleAI, BS @Berkeley_EECS. Trying to understand stuff.
yidingjiang.github.io
Joined December 2015
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  • Pinned
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    Yiding Jiang
    @yidingjiang
    Jan 7
    Information theory often gives unintuitive conclusions when it comes to data. Many of these inconsistencies can be resolved elegantly if we limit the amount of computation the observers can use. Very happy to finally introduce our work on epiplexity! 1/🧵
    user avatar
    Marc Finzi
    @m_finzi
    Jan 7
    1/🧵 We are very excited to release our new paper! From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence arxiv.org/abs/2601.03220 with amazing team @ShikaiQiu @yidingjiang @Pavel_Izmailov @zicokolter @andrewgwils
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  • user avatar
    Yiding Jiang
    @yidingjiang
    Jul 3
    Heading to Seoul for ICML ✈️ Haven’t made any particular plans but looking forward to meeting new and old friends. If anyone wants to chat about generalization, RL/exploration, epiplexity or anything else, please DM or email! My coauthors will also be giving an talk on MaxRL 👇
    user avatar
    Fahim Tajwar
    @FahimTajwar10
    Feb 5
    Are we done with new RL algorithms? Turns out we might have been optimizing the wrong objective. Introducing MaxRL, a framework to bring maximum likelihood optimization to RL settings. Paper + code + project website: zanette-labs.github.io/MaxRL/ 🧵 1/n
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    Yiding Jiang
    @yidingjiang
    Jun 25
    Behnam is one of the first people I worked with when I started doing research. I learned how to be a rigorous scientist and an ambitious researcher from him. Can’t wait to see what they will do at Mirendil. Congrats Behnam!
    user avatar
    Behnam Neyshabur
    Mirendil
    @bneyshabur
    Jun 24
    Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others.
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    Yiding Jiang
    @yidingjiang
    Nov 4, 2025
    Skills are useful abstractions for transferring useful behavior across settings, but they often need subtle tweaks for new problems. How can we learn such flexible skills? Check out @vedant_gupta_16 's thread on our end-to-end discovery of these skills! 🤖
    user avatar
    Vedant Gupta
    @vedant_gupta_16
    Nov 4, 2025
    Excited to introduce DEPS (Discovery of GenEralizable Parameterized Skills) at #NeurIPS2025! DEPS learns interpretable parameterized skills that drastically improve generalisation to unseen tasks, especially in data-constrained settings and on out-of-distribution tasks. (1/n)
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    Yiding Jiang
    @yidingjiang
    Sep 30, 2025
    What’s the minimum description length of a model trained with AlphaZero?
    20–40 MB20%
    ≥ 100 MB20%
    40–80 MB10%
    ≤ 10 MB50%
    10 votesFinal results
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