VQV Signal
According to a post on X by @Yudong_Rao, PEAR is a post-training engine that took a 27B model from 0 to 18 out of 30 successes on a real robot it had never touched, using zero demonstrations. The post credits @songhan_mit with presenting PEAR. A related post from @jmtang42 says the team trained an open 27B Qwen model to drive robot arms, a kind of control the post says others have shown with closed models.
According to a post on X by @Yudong_Rao, PEAR is a post-training engine that took a 27B model from 0 to 18 out of 30 successes on a real robot it had never touched, using zero demonstrations. The post credits @songhan_mit with presenting PEAR. A related post from @jmtang42 says the team trained an open 27B Qwen model to drive robot arms, a kind of control the post says others have shown with closed models.
AI-assisted summary based on the listed source.
@songhan_mit presented PEAR, a post-training engine that took a 27B model from 0 to 18 out of 30 successes on a real robot it had never touched, using zero demonstrations. A related post from @jmtang42 says the team trained an open 27B Qwen model to drive robot arms, the kind of control others have shown with...
If the reported results hold, they would suggest open models can be adapted to physical robot control without task-specific demonstrations. Robotics teams and builders of open models would want the full evaluation details behind the 18 of 30 figure. Watch for published methods and whether other groups can reproduce the result.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to Scobleizer News Pulse (X).
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