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DPO vs GRPO vs RL for Reasoning
DPO vs GRPO vs RL for Reasoning
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RL for reasoning (e.g., DeepSeek-R1, OpenAI o1) uses VERIFIABLE rewards. Why is this important?
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A.
Verifiable rewards eliminate the need for a reference model, unlike regular RL fine-tuning
B.
Tasks like math and code have programmatically checkable answers (unit tests, computation), so reward is unambiguous and not subject to reward-model errors
C.
Verifiable rewards require ground-truth labels for every training example, unlike RLHF which doesn't
D.
Verifiable rewards are computed by neural networks, making them faster than rule-based rewards
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