VC-Tooler-RL

Reinforcement-learning data for VC-Tooler: Learning Compositional and Adaptive Visual Tool Use.

🔗 Links

• 📄 Paper: arXiv
• 🌐 Project Page: w1zheng.github.io/VC-Tooler
• 🤗 Hugging Face: VC-Tooler-SFT · VC-Tooler-RL (this dataset)
• 🧩 ModelScope: VC-Tooler-SFT · VC-Tooler-RL (this dataset)

This dataset is the Stage II (agentic RL) data used to refine the cold-started VC-Tooler policy through interaction with a tool environment. Unlike the SFT bank, these records are not pre-generated trajectories: each is a verifiable query that the policy rolls out against live tools during training, receiving reward from the outcome, the output format, and its tool-use behavior.

What's in this dataset

Each record is an RL training instance: a visual query with a checkable ground-truth answer and the tool context needed to attempt it. During training, the policy interleaves reasoning with tool calls (ReAct-style), and rollouts are scored to optimize accurate, efficient, and context-aware tool use.

Training setup (for context)

VC-Tooler is optimized with GRPO using three rewards:

• Accuracy reward — whether the final answer is correct (the primary signal, enabled by the verifiable answers in this dataset).
• Format reward — whether the output is well-formed and the tool-call protocol is respected.
• Tool reward — a lightweight critic that inspects a rollout and rewards faithful use of tool feedback, primarily whether returned observations are incorporated into subsequent reasoning while redundant or non-progressing calls are avoided.

How it was built

RL instances are drawn from existing multimodal reasoning and tool-use RL sources, including ChartVerse, DeepEyes, DRIM, and VisualProbe. They are restricted to examples with reliably verifiable answers. To improve optimization stability, unsalvageable instances (e.g., unverifiable or consistently unsolvable prompts) are filtered out before policy training.

Intended use

• Agentic reinforcement learning (GRPO) for multimodal tool-use policies, starting from a tool-use cold-started checkpoint.
• Research on reward design for tool use, compositional multi-step reasoning, and generalization to richer or novel tool settings.

This dataset is the RL counterpart to the supervised VC-Tooler-SFT bank; the two are designed to be used in sequence (cold-start SFT, then RL).

Data format

The schema follows the verl RLVR convention:

Field	Type	Description
prompt	list of struct {role: string, content: string}	The conversation seed. See the warning below.
images	list of struct {bytes: binary, path: string}	Image(s) for the query, embedded inline. path is empty.
data_source	string	Upstream corpus the instance came from.
ability	string	Coarse task domain (e.g. chart).
env_name	string	Tool environment the rollout is executed against; vc-tooler for every row.
reward_model	struct {ground_truth: string, style: string}	Verifiable target for the accuracy reward. style is rule throughout.
extra_info	struct {question, answer, index, split, tool_folder}	question is the raw query text, answer duplicates the ground truth, index is the upstream instance id, tool_folder points at the tool definitions.

License

Released under CC BY-NC 4.0 (non-commercial). Individual source datasets retain their own licenses and terms of use; please review and comply with the terms of each upstream source before use.

Citation

@inproceedings{vctooler2026,
  title     = {VC-Tooler: Learning Compositional and Adaptive Visual Tool Use},
  author    = {Wu, Yizheng and Hua, Jiashen and Deng, Bing and Ye, Jieping},
  booktitle = {arXiv},
  year      = {2026}
}