Reevaluating Large Language Models Beyond Next-Token Prediction Mechanisms

The article challenges the common perception of large language models (LLMs) as mere next-token predictors, emphasizing that post-training techniques like RLVR enable them to learn from generated sequences, not just existing data. This shifts their purpose from prediction to reward-based optimization, altering their fundamental functionality.

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