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

Octi Zhang, Mateo Guaman Castro, Patrick Yin, Ignacio Dagnino, Abhishek Gupta et al.|October 8, 2026arXiv

Key Takeaway

Adaptive curriculum sampling that concentrates on task difficulty at the edge of a robot's capabilities makes massive parallel RL training practical and effective, solving problems that uniform sampling cannot.

Summary

This paper tackles a key challenge in scaling reinforcement learning for robots: when training with millions of parallel simulations, most experience gets wasted on tasks the robot either already mastered or can't attempt yet.

training efficiency agents

Key Terms

sim-to-real-transfer curriculum-learning exploration-exploitation-tradeoff parallel-simulation adaptive-sampling