ESamp trains a test-time distiller to model LLM depth-wise representation transitions and biases decoding toward high prediction-error paths to increase semantic diversity.
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Large Language Models Explore by Latent Distilling
ESamp trains a test-time distiller to model LLM depth-wise representation transitions and biases decoding toward high prediction-error paths to increase semantic diversity.