# VibeTraining-Bench Toolathlon Track Training Data (DIVE mix-8000)
**8,000 verified multi-turn tool-calling trajectories**, released as the training data for the **Toolathlon (multi-turn tool use) track** of [VibeTraining-Bench](https://github.com/D2I-ai/VibeTraining-Bench).
In the Toolathlon track, a research agent starts from an initial agent system and improves it under a fixed resource budget — typically by fine-tuning on this dataset — before the improved system is evaluated on [Toolathlon](https://github.com/hkust-nlp/Toolathlon) ([arXiv:2510.25726](https://arxiv.org/abs/2510.25726)).
The data was built with the [DIVE](https://arxiv.org/abs/2603.11076) recipe: **execution-grounded agentic task synthesis**, in which real tools are executed first to collect evidence, tasks are reverse-derived from the resulting traces, and trajectories are produced by solving the tasks against the live tool environments. Each task defines verifiable checkpoints, and trajectories were screened by the pipeline's verification stage before release.
## About VibeTraining-Bench
This dataset is used within [VibeTraining-Bench](https://github.com/D2I-ai/VibeTraining-Bench) ([arXiv:2610.26342](https://arxiv.org/pdf/2610.26342)), a benchmark that evaluates whether research agents can improve an agent system as a whole by co-designing its training data, the trainer, and the inference harness for iterative system improvement.
- Paper: [arXiv:2610.26342](https://arxiv.org/pdf/2610.26342) | [Hugging Face Papers](https://huggingface.co/papers/2610.26342)
- Project site: [d2i-ai.github.io/VibeTraining-Bench](https://d2i-ai.github.io/VibeTraining-Bench/index.html)
- GitHub: [D2I-ai/VibeTraining-Bench](https://github.com/D2I-ai/VibeTraining-Bench)
## Dataset Summary
| | |
|---|---:|
| Records | 8,000 |
| Total tool calls | 92,953 |
| Distinct tools | 356 |
| Messages per record | median 23 · mean 26.2 · range 6–101 |
| Tool calls per record | median 10 · mean 11.6 · max 49 |
| Tool definitions per record | median 28 · mean 29.2 · max 84 |
| File | `dive-mix8000.jsonl` (~513 MB) |
Statistics are computed over all 8,000 records in the release snapshot.
### Domain composition
Each record is assigned to the domain contributing the majority of its tool definitions:
| Domain | Records | Share | Example tools |
|---|---:|---:|---|
| Academic search | 2,475 | 30.9% | OpenAlex, Crossref, arXiv, Semantic Scholar |
| Medical | 2,137 | 26.7% | RxNorm, ICD-9/10, clinical knowledge APIs |
| Biological | 1,850 | 23.1% | Biopython-style sequence analysis, NCBI, PDB, ExPASy |
| Financial | 1,538 | 19.2% | Market and financial-statement data sources (incl. China A-share datasets) |
53.1% of records additionally include general-purpose tools: web `search`, `parallel_search`, `browse`, and `code_execution`.
Tasks are predominantly in English; the financial subset contains many Chinese-language tasks against China A-share market data (`language: en, zh`).
## Data Format
Each line of `dive-mix8000.jsonl` is one record with two fields:
- **`messages`** — a list of OpenAI chat-completions-style messages (`system` / `user` / `assistant` / `tool`)
- **`tools`** — a **JSON-serialized string** of OpenAI function definitions (`[{"type": "function", "function": {...}}, ...]`); parse it with `json.loads` before use
Conventions:
- Every record is a single-turn task: one `system` message, one `user` message, then alternating `assistant` (with `tool_calls`) and `tool` messages, ending with a final assistant answer.
- Assistant messages embed a reasoning block (` ... `, Qwen-style) followed by the visible answer. Strip or keep it depending on your chat template.
- `tool_calls[].function.arguments` are JSON **objects**, not strings.
- `tool` messages carry the executed function `name` and the matching `tool_call_id`.
Abbreviated example:
```json
{
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "A 45-year-old patient with confirmed MEN type IIA presents with a malignant carcinoid tumor of the duodenum..."},
{"role": "assistant", "content": "\nThe user asks for ICD-9 codes...\n\n",
"tool_calls": [{"id": "call_0", "type": "function",
"function": {"name": "icd9cm_diagnosis_codes", "arguments": {"terms": "carcinoid duodenum"}}}]},
{"role": "tool", "name": "icd9cm_diagnosis_codes", "tool_call_id": "call_0", "content": "..."},
{"role": "assistant", "content": "\n...\n\nThe ICD-9 codes are 209.01, 259.2, and 258.02."}
],
"tools": "[{\"type\": \"function\", \"function\": {\"name\": \"icd9cm_diagnosis_codes\", \"description\": \"...\", \"parameters\": {...}}}]"
}
```
### Relation to Toolathlon evaluation
All tasks in this dataset were synthesized by the DIVE pipeline against public data APIs (OpenAlex, Crossref, arXiv, RxNorm, NCBI/Biopython, financial data sources, etc.). They are **independent of the Toolathlon evaluation task set**, so training on this dataset does not leak Toolathlon test tasks. The dataset provides execution-grounded, multi-turn tool-calling supervision in a compatible format; it is not a collection of in-distribution Toolathlon tasks.
## Limitations and Bias
- **Synthetic data.** Trajectories are LLM-generated within the synthesis pipeline. Checkpoint-based verification filters out failing trajectories but does not guarantee that every surviving step is flawless.
- **Snapshot-dependent environments.** Tool outputs were captured at synthesis time. Time-sensitive sources (e.g., market data referencing specific trading days) are not reproducible against live APIs.
- **Domain coverage.** The tool environments differ from Toolathlon's MCP-server environments; expect gains in general multi-turn tool-calling competence rather than benchmark-specific priors.
- **Reasoning format.** Assistant turns contain `` blocks; training code must handle them explicitly.
## Intended Use
- Supervised fine-tuning of LLM agents for multi-turn tool calling and reasoning.
- The data resource for the Toolathlon track of VibeTraining-Bench, where research agents co-design data, trainer, and harness.
- Studies of execution-grounded agentic task synthesis and verification filtering.
## Citation
If you use this dataset, please cite the VibeTraining-Bench paper:
```bibtex
@misc{peng2026vibetrainingbench,
title = {VibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System Improvement},
author = {Ruotian Peng and Yuanyi Ren and Shuaiqi Liu and Xin Guo and Fang Zhu and Yangyi Huang and Yue Wu and Zheng Wang and Yandong Wen and Zhihang Fu},
year = {2026},
eprint = {2610.26342},
archivePrefix = {arXiv},
primaryClass = {cs.AI}
}
```
```bibtex
@article{202608.0051,
doi = {10.20944/preprints202608.0051.v1},
url = {https://www.preprints.org/manuscript/202608.0051},
year = 2026,
month = {August},
publisher = {Preprints},
author = {Shuaiqi Liu and Zhengkai Lin and Yuxiang Zhang and Yuanyi Ren and Yue Wu and Yongbin Li and Zheng Wang and Zhihang Fu and Jieping Ye},
title = {The Path to Recursive Self-Improving Agents: Foundation, Framework, and Future Directions},
journal = {Preprints}
}
## Acknowledgements
This dataset was produced with the DIVE synthesis recipe and its open domain toolsets. We thank the DIVE team and the Toolathlon organizers for the underlying benchmark.