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D1 LLM API Basics: messages/roles, Tokens, Streaming, Temperature; What an Agent Actually Is

• What do temperature and top_p control, and when would you use 0 versus 0.7?temperature 和 top_p 分别控制什么？什么场景用 0，什么场景用 0.7？

  Common overseasBasic#llm-basics#sampling

  How to reason about it · think before answering

  1. Establish that both act on the same next-token distribution but in different ways — that is the discriminator.
  2. temperature rescales the whole distribution; top_p truncates it to the smallest set reaching cumulative probability p.
  3. Hence the practical rule: tune one, not both, or you cannot attribute a regression.
  4. Choose by reproducibility, not by vibes: tool arguments, classification and structured output must be reproducible, so use 0.
  5. Add the agent angle: planning and tool-calling steps stay cold; only the final user-facing prose warrants higher values.

  分析过程 · 先想清楚再作答

  1. 先说清两者作用在同一个地方——模型算出的下一个 token 概率分布——但作用方式不同，这是区分度所在。
  2. temperature 是缩放整个分布：越低越尖锐、越确定；top_p 是截断——只保留累计概率达到 p 的那一小圈候选再采样。
  3. 由此推出实践建议：一般只调其中一个，两个同时调会互相干扰，出了问题分不清是谁造成的。
  4. 选值不按「创意程度」凭感觉，按「这一步的输出要不要可复现」来定：工具参数、分类判断、结构化输出必须可复现，用 0。
  5. 补一句 Agent 视角：Agent 的规划与工具调用环节几乎都用低温，只有最终面向用户的自然语言回复才考虑调高。

  Key points

  • temperature rescales the next-token distribution: lower is more deterministic, higher more random
  • top_p samples only from the smallest set whose cumulative probability reaches p; tune one, not both
  • Use ~0 for structured output, tool arguments and classification to keep results reproducible
  • Use 0.7–1.0 for creative writing; planning steps in production agents usually stay low

  答题要点

  • temperature 缩放下一个 token 的概率分布：越低越确定，越高越随机
  • top_p 只从累计概率达到 p 的候选里采样，是另一种截断随机性的方式；一般只调其中一个
  • 结构化输出、工具参数、分类判断用 0 或接近 0，保证可复现
  • 创意写作、头脑风暴用 0.7–1.0；生产 Agent 的规划步骤通常也偏低温