Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| 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}, | |
| } | |
| ``` | |