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arXiv Machine Learning By Octi Zhang, Mateo Guaman Castro, Patrick Yin, Ignacio Dagnino, Abhishek Gupta, Rosario Scalise, Byron Boots

A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control

Read the original on arXiv Machine Learning →

The paper introduces Success Guided Sampling (SGS), an adaptive sampler that focuses reinforcement learning on task configurations near the policy’s current frontier, thereby reducing wasted experience in massively parallel simulation. SGS enables large‑scale RL to solve complex quadruped locomotion and contact‑rich assembly tasks that previous methods could not, using up to one million parallel environments. The authors also distill the learned policies into RGB‑based models that transfer zero‑shot to real‑world assembly tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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