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Nick Haber
@nickhaber
Interactively learning AI, cognitive models, learning tools. Assistant Professor at Stanford.
autonomousagents.stanford.edu
Joined March 2009
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  • user avatar
    Nick Haber
    @nickhaber
    Aug 4
    So excited to be part of this workshop! If you're interested in the future of human-AI interaction, take a look, and consider submitting.
    user avatar
    Hua Shenโœจ
    @huashen218
    Aug 3
    ๐Ÿš€Our BiAlign Workshop @NeurIPS2026 is out! Call for Papers on Dynamic Alignment in Human-AI Coupled Systems ๐Ÿ‘ซ<->๐Ÿค–! AI is no longer a one-shot tool โ€” it's a long-term participant in high-stakes domains. AI shapes human trust, values, and behavior; humans reshape AI's
  • user avatar
    Nick Haber
    @nickhaber
    Jul 10
    Excited to have this super fun collaboration, led by @emilyzjin, with @joycjhsu, Yiqing Xu, @Weiyu_Liu_, and @jiajunwu_cs, out!
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    Emily Jin
    @emilyzjin
    Jul 10
    During long-horizon task planning, a robot must decide what to do, while ensuring each action is geometrically feasible. To this end, we propose ๐—ฝ๐—น๐—ฎ๐—ป๐—ป๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—น๐—ฒ๐—ฎ๐˜ƒ๐—ฒ๐—ฑ ๐˜ƒ๐—ถ๐˜€๐—ถ๐—ผ๐—ป-๐—น๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐˜๐—ต๐—ผ๐˜‚๐—ด๐—ต๐˜๐˜€.
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  • user avatar
    Nick Haber
    @nickhaber
    Jul 7
    So excited to see this out! As an advisor to @LearnWithEllo (and friend to human gems @CatalinVoss @t_sayer for years), I've had the privilege of watching Ello carefully orchestrate thoughtful design, learning sciences, and AI engineering. Psyched to see where Ello 2.0 goes.
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    Catalin Voss
    @CatalinVoss
    Jul 7
    Everyone's asking what AI can do for their job. We asked if it could teach a 6-year-old. Introducing Ello 2.0: reading, math & more for ages 4-9. Free tier for all. If we want AI to force-multiply humanity, let's start with teaching our youngest.
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  • user avatar
    Nick Haber
    @nickhaber
    Jun 16
    Great to have this out! Led by @ZiyuX in collaboration with @setlur_amrith @ChaseBlagden @aviral_kumar2 Encourage exploration in RLVR by basing a reward on the privileged information of a reference solution.
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    Violet X.
    @ZiyuX
    Jun 16
    ๐Ÿงต(1/9) Sparse RL for reasoning has an exploration problem. It can only reward solutions the model already stumbles into. On hard problems, that means lots of zeros and very little signal. SFT and self-distillation attack this with reference solutions as targets to match.