[KongchangAI]
Concept核采样

top_p

A hyperparameter on the large-model invocation side that controls the probability threshold of the candidate token set through nucleus sampling, affecting the diversity and stability of outputs.

Timeline (last 90 days)

Oct 22

When temperature, top_p and top_k are used in combination, their effects are hard to predict, and this is the main source of output style drift for the same prompt under different parameters.

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Oct 19

top_p is clamped on Flash: in thinking mode anything below 0.95 is treated as 0.95, and in non-thinking mode it is fixed at 1.0.

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Oct 19

The first of the four blueprint layers is tightening model constraints, recommending temperature ≤0.7, with strict data extraction and code generation recommended at close to 0.0–0.2.

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Oct 18

Common hyperparameters include temperature (controls output randomness, the lower the more deterministic), max tokens (limits generation length), top_p, and so on.

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Oct 12

temperature, top_p, and max_tokens are common across most models, but some reasoning models do not accept temperature, or have special semantics for max_tokens.

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Sep 19

Top-p (nucleus sampling) filters out extremely low-probability outputs by truncating the long tail of the probability distribution.

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