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WarpSAC redefines off-policy RL by tailoring stabilizers to data availability, achieving up to 96.4% success rates in challenging environments.
Plasticity loss in RL isn't just about forgetting; it's about vanishing gradients, and a simple sample re-weighting can bring back the learning.
