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Mode collapse was originally a term in generative models (such as GANs), referring to the generator tending to produce only a few kinds of samples and being unable to cover all modes of the data distribution; in the reinforcement learning context it manifests as the policy converging prematurely onto a few high-reward trajectories.
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10/22/2026
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