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Selective distillation can unlock critical learning signals in reinforcement learning, leading to significant performance gains in complex tasks.
RL's superior generalization isn't about brute force, but about carefully sculpting a few key features while preserving the base model's knowledge, unlike SFT's rapid specialization.
