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Training stability is crucial in reinforcement learning, because policy updates, sparse rewards, and environment randomness all cause curve fluctuations, and some self-distillation methods may exhibit a sudden drop in reward, entropy collapse (the policy converging prematurely to a single behavior), or abnormal gradients midway through training.
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10/15/2026
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