RLM 作者提出 Speculative Programmatic Tool Calling(sPTC)方案,针对智能体 harness 中工具调用必须等代码生成完毕才执行的等待问题,提前对环境的副本发起安全调用,让工具延迟与 token 生成重叠,错误猜测则被丢弃。目前实测加速为 1 到 1.2 倍。
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.
来源:@omarsar0 · x.com