Unverified50% confidenceSolutionExact time
PPO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
1
Sources
50%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
UnverifiedPPO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)81% similarUnverifiedDPO的核心洞见是语言模型本身可视为隐式奖励函数，因此可直接对偏好对进行对比学习，绕过显式奖励模型66% similarVerifiedRLHF trains a reward model by collecting human preference ratings, then fine-tunes the language model using reinforcement learning algorithms such as PPO; however, the reward model can be deceived by adversarial inputs and cannot enumerate all variants of harmful request expressions.66% similarUnverifiedPPO in RLHF scenarios requires simultaneously maintaining four neural networks: the policy model, reference model, reward model, and value model.66% similar
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