Adaptive samplers may keep long AI outputs from falling into loops
An author of a paper they say was accepted to NeurIPS 2026 argues that the method used to choose each word can contribute to repetition. In their tests, human raters preferred the adaptive P-less sampler to top-p nine times out of ten.
TLDR
An author says their NeurIPS 2026-accepted paper tested ten open models on outputs of up to 64K tokens. They argue that common sampling methods can contribute to loops in long writing, while adaptive methods kept the tested outputs readable. Human raters preferred P-less over top-p nine times out of ten. Whether the finding holds for long AI coding runs remains an open question.
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