# SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use

arXiv: https://arxiv.org/abs/2609.06124 · HTML: https://mlsanity.com/paper/2609.06124/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 23 of 23)

- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation](https://mlsanity.com/paper/2503.07826/) (2025)
- [ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models](https://mlsanity.com/paper/2503.00564/) (2025)
- [ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis](https://mlsanity.com/paper/2410.18447/) (2024)
- [ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset](https://mlsanity.com/paper/2511.15718/) (2025)
- [On the Robustness of Agentic Function Calling](https://mlsanity.com/paper/2504.00914/) (2025)
- [CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use](https://mlsanity.com/paper/2602.12268/) (2026)
- [Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text](https://mlsanity.com/paper/2601.10355/) (2026)
- [Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges](https://mlsanity.com/paper/2505.13328/) (2025)
- [Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions](https://mlsanity.com/paper/2509.18847/) (2025)
- [ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction](https://mlsanity.com/paper/2508.12685/) (2025)
- [Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors](https://mlsanity.com/paper/2601.15625/) (2026)
- [FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use](https://mlsanity.com/paper/2510.24645/) (2025)
- [Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing](https://mlsanity.com/paper/2512.23611/) (2025)
- [From Failure to Mastery: Generating Hard Samples for Tool-use Agents](https://mlsanity.com/paper/2601.01498/) (2026)
- [ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas](https://mlsanity.com/paper/2601.21558/) (2026)
- [Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning](https://mlsanity.com/paper/2604.09813/) (2026)
- [CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification](https://mlsanity.com/paper/2603.01940/) (2026)
- [UI-KOBE: Knowledge-Oriented Behavior Exploration for Lightweight Graph-Guided GUI Agents](https://mlsanity.com/paper/2605.29534/) (2026)
- [User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale](https://mlsanity.com/paper/2601.08225/) (2026)

## Built on by (most cited first, 1 of 1)

- [SWEPatchTest: Evaluating Software Tests Beyond Reproduction](https://mlsanity.com/paper/2610.18004/) (2026)

## Abstract (arXiv)

High-quality multi-turn tool-use data is essential for training agentic models, yet existing data synthesis methods often underrepresent the argument-level dependencies that are critical to long-horizon tool use. As a result, even when a model selects the correct tool, task execution may still fail because the model fills tool arguments with fabricated, stale, or weakly grounded values. To address this problem, we propose State-Guided Data Synthesis with Argument Provenance (SAP). SAP combines state guidance, tool-argument provenance constraints, and turn-level validation to efficiently construct tool-use trajectories with long-range dependencies and high accuracy. Using data generated by SAP, we build SAP-4B, which is highly competitive even when compared with much larger models across multiple benchmarks. Source code, synthesized data, and trained weights are available at https://github.com/Zichen1024/SAP.