Unverified50% confidenceFactExact time
Temperature changes how concentrated or spread out the token probability distribution is during sampling; lower temperatures make choices more predictable, while higher temperatures allow more variation, but it does not change the knowledge the model has learned.
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10/19/2026
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Unverified温度超参数τ越小分布越集中，模型对难负样本越敏感，但τ过小会导致梯度对异常值敏感、训练不稳定78% similarVerifiedTemperature controls the flatness of the sampling distribution; a higher value tends toward selecting lower-probability words, while Top-p dynamically narrows the candidate vocabulary set through a cumulative probability threshold.77% similarVerifiedThe higher the temperature parameter, the flatter the probability distribution output by Softmax, and the more creative and diverse the output; the lower the temperature, the sharper the distribution and the more deterministic and conservative the output.77% similarUnverifiedAgent behavior is non-deterministic; even using the same prompt and temperature=0 settings, the same model's outputs at different points in time may exhibit subtle differences, influenced by factors such as floating-point arithmetic precision, batching strategies, and model version updates.74% similar
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