Updating computational workflow using trace feedback

A method for use with a computing system, the method comprising:

• receiving context data;
• obtaining a workflow graph of a computational workflow, wherein:
• the computational workflow includes a plurality of workflow nodes that each include a respective adjustable parameter;
• and
• the workflow graph is structured as a directed acyclic graph (DAG);
• processing a workflow input at the computational workflow to obtain a workflow output;
• selecting an adjustable parameter included in the computational workflow;
• computing a trace feedback including:
• an execution trace of the processing of the workflow input starting at a selected workflow node that includes the selected adjustable parameter, wherein the execution trace specifies a subgraph of the DAG;
• and
• an output feedback received in response to the workflow output;
• computing a parameter update to the selected adjustable parameter based at least in part on the context data and the trace feedback;
• and
• applying the parameter update to the selected adjustable parameter.

A computing system comprising:

• one or more processing devices configured to:
• receive context data, wherein the context data is a text input that specifies an output objective of a computational workflow;
• obtain a workflow graph of the computational workflow, wherein the computational workflow includes a plurality of workflow nodes that each include a respective adjustable parameter;
• process a workflow input at the computational workflow to obtain a workflow output;
• select an adjustable parameter included in the computational workflow;
• compute a trace feedback including:
• an execution trace of the processing of the workflow input starting at a selected workflow node that includes the selected adjustable parameter, wherein the execution trace specifies a subgraph of the workflow graph;
• and
• an output feedback received in response to the workflow output,
• wherein the trace feedback is a text feedback;
• compute a parameter update to the selected adjustable parameter based at least in part on the context data and the trace feedback, wherein the parameter update is computed at least in part at a language processing machine learning model;
• and
• apply the parameter update to the selected adjustable parameter.