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hijkzzz avatar hijkzzz commented on July 30, 2024

@Ashura5 就是这个?https://github.com/OpenRLHF/OpenRLHF/blob/main/examples/scripts/train_iterative_dpo_llama.sh

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Ashura5 avatar Ashura5 commented on July 30, 2024

这个是在训练之前从reference model中推理数据,reward model打分
我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据

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hijkzzz avatar hijkzzz commented on July 30, 2024

这个是在训练之前从reference model中推理数据,reward model打分 我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据

这个实现的就是每轮用最新的checkpoint 推理数据打分,只不过batch size 默认设置的比较大。如果要实现PPO那种完全online的会复杂一点,可以参考train_ppo_ray.py的设计

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Ashura5 avatar Ashura5 commented on July 30, 2024

这个是在训练之前从reference model中推理数据,reward model打分 我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据

这个实现的就是每轮用最新的checkpoint 推理数据打分,只不过batch size 默认设置的比较大。如果要实现PPO那种完全online的会复杂一点,可以参考train_ppo_ray.py的设计

好的,谢谢,我再仔细看看这一段

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