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    SEAR study tests whether robot agents learn from practice or just try more often

    The SEAR team says the study covers seven frontier LLMs, 330 simulated tasks and five real-robot tasks.

    Wenhu ChenWC
    Xinyi WangXW
    2 Sources, 4h ago, first seen 4h ago

    TLDR

    SEAR’s researchers say they set out to test whether rising success rates reflect learning or repeated attempts, and whether saved skills and memories help on new tasks, in another simulator or on a real robot. They say the agent can write code that becomes a robot’s policy, act live in the control loop or train its own VLA. The study uses a matched control for every comparison and a sealed judge on unseen scenes.

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    2 Sources, first seen 4h ago

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    Combined views

    1.4K

    2 Sources, first seen 4h ago

    19 likes3 comments11 saves11 reposts
    Image: @XinyiWang98 on X

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    2 Sources

    Xinyi Wang@XinyiWang98An LLM robot agent’s success rate goes up as it practices. Did it learn anything, or did it just try more often? And do the skills and memories it saves still help on new tasks, in a new simulator, on a real robot? We built SEAR to answer both. To our knowledge, it’s the first large scale controlled study of self-evolving robot agents. The paper is now on arXiv 📄 The agent improves a robot in one of three ways: it writes code that becomes the policy, it acts live in the control loop, or it trains its own VLA. 7 frontier LLMs, 330 sim tasks, 5 real-robot tasks, 4+ years of agent runtime, with a matched control for every comparison and a sealed judge on unseen scenes. A few findings surprised us 🧵 1/7 http://arxiv.org/abs/2610.187924h
    Wenhu Chen@WenhuChenRT @XinyiWang98: An LLM robot agent’s success rate goes up as it practices. Did it learn anything, or did it just try more often? And do th…1h
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    2 Sources

    Xinyi Wang@XinyiWang98An LLM robot agent’s success rate goes up as it practices. Did it learn anything, or did it just try more often? And do the skills and memories it saves still help on new tasks, in a new simulator, on a real robot? We built SEAR to answer both. To our knowledge, it’s the first large scale controlled study of self-evolving robot agents. The paper is now on arXiv 📄 The agent improves a robot in one of three ways: it writes code that becomes the policy, it acts live in the control loop, or it trains its own VLA. 7 frontier LLMs, 330 sim tasks, 5 real-robot tasks, 4+ years of agent runtime, with a matched control for every comparison and a sealed judge on unseen scenes. A few findings surprised us 🧵 1/7 http://arxiv.org/abs/2610.187924h
    Wenhu Chen@WenhuChenRT @XinyiWang98: An LLM robot agent’s success rate goes up as it practices. Did it learn anything, or did it just try more often? And do th…1h
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