| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - agent |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use |
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| **paper:** [SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use](https://arxiv.org/abs/2609.06124) |
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| ## Dataset Overview |
| This is a high-quality, executor-validated multi-turn tool-use dataset designed for training agentic language models on long-horizon function calling tasks. The dataset focuses on **argument-level cross-turn dependency grounding**, ensuring tool arguments are sourced from verifiable upstream context (initial state, prior tool returns, or user messages) rather than fabricated values. |
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| Three stages: |
| - (1) FSM skeleton synthesis by A_FSM with provenance tags |
| - (2) per-call planning + executor execution by A_plan + ε (two-track output: executor args θ_exec + provenance metadata θ_prov, parallel grouping, per-call retry) |
| - (3) post-hoc dialogue synthesis by A_msg |
| |
| ## Basic Statistics |
| | Item | Details | |
| |------|---------| |
| | Total trajectories | **9,234** | |
| | Task type | Multi-turn tool use / agent function calling | |
| | Validation | All trajectories verified against live tool executors | |
| | Supported benchmarks | BFCL v4 Multi-Turn, τ²-bench | |
| |
| ## Reference Training Setup |
| The dataset was originally used to train a 4B parameter model via pure SFT (no RL): |
| - Base model: Qwen3-4B-Instruct |
| - Optimizer: AdamW, learning rate 1e-6 |
| - Batch size: 128, 10 training epochs |
| - Training framework: verl |
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