Unverified50% confidenceFactExact time
PPO用于语言模型对齐时涉及四个需要同时维护的模型实例：策略模型(Actor)、冻结的SFT参考模型、奖励模型和评价模型(Critic)
1
Sources
50%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
UnverifiedPPO在语言模型对齐中面临奖励欺骗挑战，工程实践中通常在目标函数中加入KL散度惩罚项约束策略模型与SFT基础模型的偏离81% similarUnverifiedPPO in RLHF scenarios requires simultaneously maintaining four neural networks: the policy model, reference model, reward model, and value model.77% similarUnverified自回归多模态模型借鉴语言模型的token预测机制，在理解复杂文字指令和保持角色一致性方面更具优势69% similarVerifiedThe RLHF pipeline consists of three steps: training a reward model to predict human preference scores, adjusting language model parameters using the PPO reinforcement learning algorithm, and using KL divergence constraints to prevent the model from deviating from its original language capabilities.65% similar
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