xiaohua616/crossvid_dataset

CrossVid Train (AgentCVR RL Training Data)

This dataset is AgentCVR training data for Script-Simulated Reinforcement Learning (RL) on the CrossVid benchmark. It is in Parquet format and can be used directly for multi-turn Agent GRPO training (e.g., with the verl framework).

Data Format

Single file: crossvid_train.parquet.

Main columns (example; refer to actual parquet columns):

Column	Description
data_source	Data source identifier, e.g. crossvid_sort, crossvid_grounding, etc.
agent_name	Agent type, e.g. tool_agent
prompt	Multi-turn dialogue list; each item is `{"role": "system"
ability	Ability label, e.g. planning
reward_model	Reward configuration, including style (e.g. rule) and ground_truth
extra_info	Extra info: task_name, task_id, videos (script descriptions), original_question, tool-related parameters, etc.

The exact column structure follows the output of AgentCVR verl/examples/data_preprocess/crossVid_tool_agent_loop.py.

Usage

1. Download: Download crossvid_train.parquet from this repo, or load via Hugging Face Datasets in your training script if configured as an HF dataset.
2. Training: In frameworks such as verl, point the data path to this parquet and use CrossVid rule-based rewards (e.g. verl/utils/reward_score/crossvid.py) for GRPO training.

Example (adjust paths for your project):

import pandas as pd
df = pd.read_parquet("crossvid_train.parquet")
# Or use datasets
# from datasets import load_dataset
# ds = load_dataset("parquet", data_files="crossvid_train.parquet")["train"]

Related Resources

• Models: Master Agents trained with this data are in the collection AgentCVR-CrossVid: qwen4b_crossvidRL and qwen8b_crossvidRL.
• Code and benchmark: AgentCVR, CrossVid.