The post-training journey of modern LLMs revisited
Read OriginalThis technical article revisits the post-training journey of modern Large Language Models (LLMs). It contrasts the established Reinforcement Learning from Human Feedback (RLHF) with the emerging paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). The piece explains how RLVR uses objective, programmatically determined rewards (like code compilation or math answer correctness) instead of subjective human preferences, aiming to create more reliable and accurate reasoning models.
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