Execution-First Synthetic Tool-Use Trace Generation for LLM Agents

2026-07-31

AI Summary

Existing query-first data synthesis approaches struggle to generate valid and executable tool-use sequences. This work proposes SyntheticAgentTraceQA, a novel framework that introduces an "execution-first" paradigm: it first constructs high-level workflows, maps and validates feasible tool trajectories, and then synthesizes corresponding natural language tasks and reference answers. The method integrates dependency-aware tool assignment, trajectory validation in a controlled environment, and reasoning-augmented annotation generation, followed by fine-tuning and evaluation using the Qwen model. Experimental results demonstrate that this framework substantially improves large language model (LLM) agents’ tool execution accuracy, trajectory consistency, and answer quality. Furthermore, the study reveals that masked supervision outperforms full supervision for models at the 9B scale.

Abstract

Agentic software-engineering and industrial systems increasingly operate through executable workflows rather than code genera- tion alone: they search artifacts, invoke tools, inspect structured observations, and query databases. Training these agents requires supervision data that captures valid tool interactions and executable workflows. However, traditional query-first data synthesis can fail because plausible user requests may not correspond to valid tool sequences, compatible parameters, or available data. To address this limitation, we propose SyntheticAgentTraceQA, an execution- first framework for generating scalable supervision data for tool- augmented agents. Our framework first constructs high-level work- flow structures, maps them to available tools through dependency- aware assignment, executes and validates the resulting traces in con- trolled environments, and only then synthesizes natural-language user tasks, teacher-generated reasoning annotations, and reference answers. We evaluate the framework across four tool ecosystems and use the resulting data to fine-tune and evaluate Qwen model variants. The results show that execution-grounded supervision improves tool execution behavior, reference-trace agreement, and answer-generation performance on the evaluated tasks. Further analysis reveals a supervision trade-off: masked supervision, which excludes reasoning annotations from the training objective, im- proves final-answer metrics, whereas full supervision, computing loss over the complete assistant output including reasoning tokens, underperforms on answer quality and does not consistently im- prove reference-trace agreement, particularly at the 9B scale. These findings highlight the importance of designing synthetic supervi- sion according to the desired capabilities of tool-augmented agents.

Problem

• tool-use
• synthetic data
• execution trace
• LLM agents
• supervision data

Innovation

• execution-first
• synthetic data generation
• tool-augmented agents
• workflow validation
• masked supervision