Verified65% confidenceFactExact time
The temperature parameter scales the Logits vector, and when temperature is 0, it degenerates into greedy decoding.
3
Sources
65%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
并非一切都值得消耗Token:何时用AI,何时用确定性代码
hackernewshackernews7/6/2026
Related Claims
Verifiedtemperature为0时退化为贪婪解码,每步选取概率最高的Token,但仅能保证单次模型版本内的确定性81% similarUnverified温度参数(Temperature)的数学本质是对Softmax函数输出的概率分布进行缩放,趋近于0时模型进行贪心解码81% similarUnverified温度参数通过对 softmax 层的 logits 进行缩放来控制概率分布的尖锐程度,温度趋近于0时接近贪心解码80% similarUnverifiedFor deterministic requests with temperature=0, semantic caching can achieve near-zero-error reuse; for requests with temperature>0, it needs to be weighed together with a similarity threshold and the business's fault tolerance.79% similarVerifiedThe Temperature parameter of large models typically ranges from 0 to 2; the lower the temperature, the more deterministic and conservative the output, while the higher the temperature, the more diverse and creative the output.78% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/258644API
curl https://kongchang.com/api/v1/knowledge/claims/258644MCP
get_claim(id=258644)