Unverified50% confidenceFactExact time
RL rollout可能长达数十万token，导致信用分配问题在长序列尺度上精度极为有限
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Cursor揭秘：递归模型改进如何让AI自我进化
Related Claims
Unverified在Meta-RL设定下奖励信号极度稀疏且延迟严重，带来巨大的信用分配难题76% similarUnverifiedRNNs face the vanishing gradient problem: the longer the sequence, the longer the chain of multiplications the gradient must pass through, causing values to shrink exponentially.62% similarUnverifiedLSTM still performs poorly when processing sequences exceeding 500 tokens, and its sequential nature means that a sequence of length N requires N time steps to complete the forward pass.61% similarUnverified低精度引入的数值扰动会在RL的多轮迭代中通过策略梯度的乘积形式被放大，可能导致训练崩溃或模型性能退化60% similarUnverified2023年多项研究证明，即使经过RLHF对齐的模型也难以完全抵抗精心设计的间接提示词注入60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/598128API
curl https://kongchang.com/api/v1/knowledge/claims/598128MCP
get_claim(id=598128)