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Post-training effectiveness depends critically on reward signal
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TLDR; post-training can escape the pre-training ceiling if you have a dense reward system. a reward of either 1 or 0 is quite bad, and a reward that is continuous from 0 to 1 is more ideal. best quote: "When training these models utilizing a standard sparse reward, we indeed find that the base model’s probability of producing the desired behavior has a strong effect on the success of RL post-training"
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