Unverified50% confidenceCautionExact time
In Adam, because the second-moment estimate has not yet stabilized in the early stages of training, the adaptive learning rate may fluctuate sharply, causing the model to converge prematurely to an undesirable singular policy region.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe author points out that the bias correction terms in Adam can be omitted from both the numerator and denominator, as their effect becomes negligible after approximately 1,000 to 2,000 training steps.71% similarUnverifiedThe AI hallucination phenomenon is caused by two reasons: the limitations of probabilistic prediction and the timeliness of training data.71% similarUnverified专项优化的AI模型在特定领域可能表现不及通用基础模型，这与深度学习中的灾难性遗忘现象有关71% similarUnverifiedTurning to AI for help too early, before engaging in sufficient independent thinking, undermines learning outcomes.69% similarUnverifiedInverse Reinforcement Learning faces an ill-posedness problem—the same set of behavioral data may correspond to multiple entirely different reward functions69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/914989API
curl https://kongchang.com/api/v1/knowledge/claims/914989MCP
get_claim(id=914989)