Unverified60% confidenceFactExact time
PPO was proposed by Schulman et al. in 2017 and constrains the policy update step size by clipping the importance sampling ratio.
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60%
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Long-term
Relevance
7/17/2026
First Seen
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Related Claims
VerifiedPPO was proposed by OpenAI in 2017 and limits the magnitude of policy updates through a clipping mechanism, truncating gradients when the probability ratio between the new and old policies exceeds the range of 1±0.2.75% similarUnverifiedPPO constrains policy updates to a neighborhood of the old policy by clipping the importance sampling ratio, approximately achieving the stability guarantees of on-policy training.73% similarUnverifiedCISPO（Clipped Importance Sampling Policy Optimization）与 PPO 同源，通过重要性采样比值的裁剪约束策略更新幅度73% similarVerifiedTRPO was proposed by John Schulman et al. in 2015, formalizing policy updates as a constrained optimization problem: maximizing a surrogate objective function while constraining the KL divergence between the new and old policies to not exceed a threshold.69% similarVerifiedThe core innovation of PPO lies in limiting the magnitude of policy updates through a clipped objective function, preventing training collapse caused by excessively large single updates.67% similar
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