AI agents have shown great potential in automating real-world tasks, such as conducting a Google Search, reading local computer files, or executing generated Python scripts. To achieve such agentic workflows, LLMs need to learn how to use tools correctly and efficiently. To teach large language models tool uses, we need datasets of tool-use chains and their corresponding user queries. In our prior work introduced in InstructPipe, we manually annotated our evaluation data, but it is impractical to scale up the human annotation for advanced LLM fine-tuning workstreams. To streamline the data workstream, prior work, e.g., ToolBench and ToolACE, explored using an agent to automatically search a tool-use path with trial and error. This representative annotation approach involves two steps: (1) generate a hypothetical user instruction from a sampled API pool, and (2) use a depth-first search (DFS) agent to find its tool-use solution. This approach is inherently inefficient because its core concept is to distill valuable trajectories from a complex agent exploration for training an LLM.
In “ToolGrad: Efficient Tool-use Dataset Generation with Textual ‘Gradients’”, presented at ACL 2026, we introduce an alternative solution paradigm. ToolGrad first generates a ground-truth tool-use chain and then annotates its corresponding user prompt. Intuitively, an explicit tool-use solution provides more unambiguous information than a prompt, making the annotation, from tool usage to the use query, much easier and requiring only one LLM step. Our result shows that our answer-first approach can generate more complex (long-horizon) tool-use data with lower cost. LLMs trained on our generated data also outperform those trained on baseline methods, and even match SoTA proprietary LLMs on out-of-distribution (OOD) datasets with unseen tools.