---
license: other
license_name: tbd
language:
  - en
task_categories:
  - text-generation
tags:
  - tool-use
  - function-calling
  - agent
  - sft
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: davinci_tooldata_open.jsonl
---

# Davinci-ToolData

<p align="center">
  📄 <a href="https://arxiv.org/abs/2610.16867">Paper</a> ·
  💻 <a href="https://github.com/MassLab-SII/Davinci-Tool">Code</a> ·
  🤗 <a href="https://huggingface.co/SII-Enigma/Davinci-Tool-30B">Davinci-Tool-30B</a> ·
  🤗 <a href="https://huggingface.co/SII-Enigma/Davinci-Tool-30B-SFT">Davinci-Tool-30B-SFT</a> ·
  🤗 <a href="https://huggingface.co/datasets/SII-Enigma/Davinci-ToolData">Davinci-ToolData</a>
</p>

This repository releases a curated, high-quality subset of **Davinci-ToolData**, the
execution-grounded tool-use corpus used to train
[Davinci-Tool-30B](https://huggingface.co/SII-Enigma/Davinci-Tool-30B). It contains
**15,166 verified multi-turn trajectories** that invoke **4,285 distinct tools**, drawn from both
data engines: EGTS demonstrations replayed and verified in independently authored environments, and
EES trajectories in synthesized single-domain and cross-domain environments. Every trajectory was
accepted only after its outcome was checked against executable evidence: a recovered state
transition, grounded answer evidence, or executable task predicates.

<p align="center">
  <img src="figures/benchmark_comparison.png" width="90%" alt="Benchmark comparison">
</p>
<p align="center"><em>Performance on τ²-bench, BFCL v4 multi-turn, and MCP-Atlas. Davinci-Tool-30B (after DPO) is hatched; its SFT checkpoint is shown for comparison.</em></p>

## Method

<p align="center">
  <img src="figures/method_overview.png" width="100%" alt="Davinci-Tool overview">
</p>
<p align="center"><em>Construction of Davinci-ToolData and training of Davinci-Tool.</em></p>

Davinci-ToolData is built by two complementary pipelines that share one execution and verification
interface:

- **EGTS — Execution-Grounded Task Synthesis** (authored systems). An explorer interacts with a
  resettable executable world; user tasks are synthesized *from* the recorded outcome; the world is
  reset and the task is replayed with a guide, and the replay is accepted only if it recovers the
  explored outcome. Structural checks, a hard factual judge, and a soft quality judge filter the
  results.
- **EES — Executable Environment Synthesis** (synthesized systems). Audited executable domains
  (191 domains in 19 categories) produce state-grounded tasks with executable verifiers within one
  domain (**EES-Single**), and are composed into shared multi-domain worlds (246 compositions) with
  shared entities, linking fields, cross-domain policies, and coherence constraints (**EES-Cross**).

Training has two stages: **SFT** on the verified demonstrations, then **decision-level DPO**, which
reuses restorable states and executable verifiers to fork the SFT policy at decision points, execute
alternative actions from the same state, and keep pairs whose verified downstream outcomes differ
(1,150 pairs).

## Main results

Models below are trained on the full Davinci-ToolData corpus (30,395 trajectories) described in the paper.

| Model | τ²-bench Retail | τ²-bench Telecom | τ²-bench Avg. | BFCL v4 MT Avg. | MCP-Atlas |
|---|---:|---:|---:|---:|---:|
| Qwen3-Coder-480B-A35B-Instruct | 77.5 | 39.5 | 58.5 | 43.5 | 28.1 |
| Kimi-K2-Instruct | 70.6 | 65.8 | 68.2 | 33.4 | 59.6 |
| MiniMax-M2.1 | 81.6 | 87.0 | 84.3 | 71.4 | 59.6 |
| GLM-4.7 | 79.8 | 98.2 | 89.0 | 65.4 | 55.1 |
| GPT-5.2 | 81.6 | 98.2 | 89.9 | 28.9 | 61.8 |
| Claude-Sonnet-4.5 | 86.0 | 98.0 | 92.0 | 55.3 | 65.2 |
| Qwen3-Coder-30B-A3B-Instruct | 68.7 | 36.0 | 52.4 | 30.4 | 16.9 |
| + Toucan-1.5M SFT | 65.8 | 89.5 | 77.7 | 41.9 | 22.5 |
| + Davinci-ToolData SFT (**Davinci-Tool-30B-SFT**) | 73.7 | 96.5 | 85.1 | 60.6 | 52.8 |
| + SFT + DPO (**Davinci-Tool-30B**) | **79.8** | **96.5** | **88.2** | **62.8** | **56.2** |

All scores are from the authors' evaluations under the official protocols; MCP-Atlas uses the 89
tasks that do not require external API keys. Under the same backbone and SFT hyperparameters,
Davinci-ToolData exceeds the public Toucan-1.5M release by 7.4 / 18.7 / 30.3 points.

**Transfer to smaller models** (SFT on Davinci-ToolData):

| Model | τ²-bench Avg. | BFCL v4 MT Avg. |
|---|---:|---:|
| Qwen3-8B → + Davinci-ToolData | 32.5 → **74.6** | 15.4 → **33.9** |
| Qwen3-14B → + Davinci-ToolData | 33.4 → **76.8** | 21.1 → **41.4** |

**Data sources are complementary** (SFT on Qwen3-Coder-30B-A3B-Instruct):

| EGTS | EES-Single | EES-Cross | τ²-bench | BFCL v4 | MCP-Atlas |
|:---:|:---:|:---:|---:|---:|---:|
| ✓ | | | 78.9 | 57.4 | 28.1 |
| | ✓ | | 82.0 | 54.5 | 29.2 |
| | | ✓ | 83.4 | 46.6 | 47.2 |
| ✓ | ✓ | | 79.4 | 57.6 | 31.5 |
| | ✓ | ✓ | 82.9 | 55.6 | 50.6 |
| ✓ | ✓ | ✓ | **85.1** | **60.6** | **52.8** |

## Dataset structure

| File | Trajectories | Distinct invoked tools | Mean tool calls | Mean assistant turns |
|---|---:|---:|---:|---:|
| `davinci_tooldata_open.jsonl` | 15,166 | 4,285 | 9.96 | 10.86 |

Each trajectory exposes 28.4 tool schemas on average (6,755 distinct declared tools in total), and
2,421 trajectories contain multiple user turns. Trajectories were generated with DeepSeek-V4-Pro.

### Format

Each line is one JSON object:

```json
{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "...",
     "tool_calls": [{"function": {"name": "...", "arguments": "{...}"}}]},
    {"role": "tool", "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "tools": [
    {"type": "function",
     "function": {"name": "...", "description": "...", "parameters": "{\"type\":\"object\", ...}"}}
  ]
}
```

- Roles are `system`, `user`, `assistant`, and `tool`.
- `tool_calls[].function.arguments` and `tools[].function.parameters` are **JSON-encoded strings**.
  Decode `parameters` with `json.loads` before passing tools to a chat template that expects objects.

### Usage

```python
from datasets import load_dataset

ds = load_dataset("SII-Enigma/Davinci-ToolData", split="train")
```

## Reproduction

The construction code (EGTS, EES-Cross) and configs are in the
[Davinci-Tool repository](https://github.com/MassLab-SII/Davinci-Tool).

## License

License to be determined. Some trajectories are derived from third-party environments that keep
their own licenses and terms.

## Citation

```bibtex
@misc{yuan2026davincitoolscalingagentictooluse,
      title={Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments}, 
      author={Xiaoyang Yuan and Ji Zhao and Xingyu Hu and Wenhan Wang and Yutao Hu and Qiaosheng Zhang and Pengfei Liu and Yu Qiao and Yi Bin and Wenqi Shao},
      year={2026},
      eprint={2610.16867},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2610.16867}, 
}
```