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LLM capabilities come mostly from imitative learning, not reinforcement learning from verifiable rewards (RLVR), despite RLVR's prominence. RLVR may use significant training compute but imparts minimal information. Evidence shows non-RLVR models can match RLVR results via sampling or ensembling, and chain-of-thought remains legible, indicating imitative learning's dominance.
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