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University of California
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Self-distillation can significantly enhance reinforcement learning stability and performance, outperforming traditional methods in sparse-reward environments.
Gated Delta Networks can achieve stable learning-rate transfer across model widths, a breakthrough that significantly enhances training efficiency for large language models.
