sPTC:推测式工具调用加速智能体推理

elvis · @omarsar0 · X·2026-08-25 04:42·16小时前
AI 导读

来自RLM作者的sPTC(Speculative Programmatic Tool Calling)技术,在代码生成时提前推测并排队工具调用,使工具延迟与token生成重叠,而非叠加。该方法在harness层实现,当前带来1至1.2倍加速,前景可观。

elvis@omarsar0
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sPTC:推测式工具调用加速智能体推理

2026-08-25 04:42· 16小时前
AI 导读

来自RLM作者的sPTC(Speculative Programmatic Tool Calling)技术,在代码生成时提前推测并排队工具调用,使工具延迟与token生成重叠,而非叠加。该方法在harness层实现,当前带来1至1.2倍加速,前景可观。

Recommended reading and a really cool idea.

There are a lot of interesting harness designs that are starting to emerge around tool calling and code execution. RLM is one of them. But so is this Speculative Programmatic Tool Calling approach (from the same author of RLM).

There are plenty of ways to gain efficiencies at the harness layer.

Harnesses make agents wait: the model streams a block of code, and tool calls inside it only run once generation finishes. sPTC launches the safe calls early against a copy of the environment, so tool latency overlaps with token generation instead of adding on top of it. Bad guesses get thrown away. So far it's 1 to 1.2x speedup. Very promising.

alex zhangIntroducing Speculative Programmatic Tool Calling (sPTC)! A general class of technique for speculating on tool calls during code generation in a harness and que...