Orsta-7B

One RL to See Them All

• 🐙 GitHub Repo: MiniMax-AI/One-RL-to-See-Them-All
• 📜 Paper (arXiv): V-Triune: One RL to See Them All (arXiv:2505.18129)
• 💾 Dataset: Orsta-Data-47k on Hugging Face
• 🌐 Project Page: https://github.com/MiniMax-AI/One-RL-to-See-Them-All

Model Overview

Orsta-7B is a cutting-edge vision-language model (VLM) designed to achieve superior performance across a wide spectrum of both visual reasoning and visual perception tasks. This model is a result of post-training with V-Triune, our novel unified reinforcement learning (RL) system.

The V-Triune system enables VLMs to be jointly optimized on diverse multimodal tasks within a single, cohesive training pipeline. Orsta-7B has been specifically trained using V-Triune on a carefully curated set of eight challenging visual tasks, fostering robust generalization and enhanced capabilities.

Training with V-Triune

Orsta-7B's advanced abilities stem from its training with the V-Triune system. Key aspects of its training include:

• Unified RL Framework (V-Triune): V-Triune is a Visual Triple-Unified Reinforcement Learning system featuring three core complementary components:

  • Sample-Level Data Formatting (to unify diverse task inputs)
  • Verifier-Level Reward Computation (to deliver custom rewards via specialized verifiers)
  • Source-Level Metric Monitoring (to diagnose problems at the data-source level)
  • It also incorporates an innovative Dynamic IoU reward mechanism, crucial for optimizing visual perception tasks. You can find more details in our paper: V-Triune

• Diverse Joint Task Optimization: Orsta-7B was jointly optimized on the following eight visual tasks:

  • Visual Reasoning Tasks: Mathematics, Science Question Answering, Chart Understanding, and Puzzle Solving.
  • Visual Perception Tasks: Object Detection, Visual Grounding, Optical Character Recognition (OCR), and Object Counting.

This comprehensive training allows Orsta-7B to develop a deeper understanding of visual content and its relation to textual prompts, excelling in tasks that require intricate reasoning and precise perception.

Performance

How to Use

Orsta-7B is developed by post-training the Qwen2.5-VL-7B-Instruct model using our V-Triune reinforcement learning system. Consequently, its core usage, particularly regarding input formatting and model interaction, largely follows the established patterns of the Qwen2.5-VL series.

For comprehensive details on the base model's capabilities, multi-turn dialogue format, image input encoding specifics, and other functionalities, we recommend referring to the official Qwen2.5-VL documentation.

Citation 🏆

If you use Orsta-7B or the V-Triune system in your research, please cite our work:

@article{ma2025one,
      title={One RL to See Them All: Visual Triple Unified Reinforcement Learning}, 
      author={Ma, Yan and Du, Linge and Shen, Xuyang and Chen, Shaoxiang and Li, Pengfei and Ren, Qibing and Ma, Lizhuang and Dai, Yuchao and Liu, Pengfei and Yan, Junjie},
      journal={arXiv preprint arXiv:2505.18129},
      year={2025}
}