Unverified60% confidenceSolutionExact time
High temperature combined with a missing repetition penalty can easily cause the model to fall into repetitive loops or generate meaningless content.
2
Sources
60%
Confidence
Long-term
Relevance
8/25/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRepetition degeneration is usually related to sampling parameter settings, including temperature being too low and improper top-p or repetition penalty settings.75% similarUnverifiedSetting the resume limit to two times and exiting with a non-zero status prevents the model from falling into a loop during repeated continuations or producing completions inconsistent with the original answer.73% similarVerifiedThe non-determinism of large language models stems from their autoregressive sampling process; even with temperature set to 0, differences in inference frameworks, batch sizes, and hardware floating-point operations can all cause output drift.70% similarUnverified主从复制中异步复制模式存在复制延迟，半同步模式降低数据丢失风险但增加写入延迟70% similar
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