Google Research has introduced ToolGrad, a method for generating tool-use datasets by applying the idea of gradients to text. In machine learning, gradients normally drive parameter updates; ToolGrad instead uses textual feedback to guide the data-generation process.
Tool-use datasets teach models to call external functions and APIs. Creating these datasets by hand is expensive, and ToolGrad is positioned as a more efficient alternative. The work is described in a Google Research blog post under the Machine Intelligence category.
The source presents ToolGrad as a response to the high cost of tool-use data, but it does not compare the method with other dataset-generation approaches.