One RL to See Them All
Model Overview
Orsta-32B-0321 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-32B-0321 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-32B-0321'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-32B-0321 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-32B-0321 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
| Model | Knowledge | Mathematics | Perception | Coding | Info. Ex. | Planning | Science | Metrics | MEGA-Bench
Core |
|---|
| QwenVL-2.5-32B-0321 | 8.48 | 12.62 | 11.99 | 13.59 | 15.44 | 8.61 | 16.78 | 14.91 | 11.87 |
| MM-Eureka-32B 💡 | 12.20 | 20.19 | 21.88 | 15.86 | 21.23 | 15.47 | 19.95 | 22.77 | 18.57 |
| VL-Rethinker-32B 💡 | 12.16 | 28.09 | 22.99 | 11.89 | 21.50 | 15.09 | 28.10 | 15.73 | 19.41 |
| Orsta-32B-0321 (Ours) 💡 | 21.33 | 28.55 | 32.23 | 19.44 | 26.38 | 17.78 | 33.20 | 24.18 | 25.94 |
| - | - | - | - | - | - | - | - | - | - |
| Δ (Ours - Backbone) | +12.9 | +15.9 | +20.2 | +5.9 | +10.9 | +9.2 | +16.4 | +9.3 | +14.1 |
How to Use
Orsta-32B-0321 is developed by post-training the
Qwen2.5-VL-32B-Instruct (0321 checkpoint) model using our V-Triune reinforcement learning system. The Qwen2.5-VL-32B-Instruct (0321 checkpoint) is a publicly available baseline known for its reliable core reasoning abilities, alongside certain recognized limitations in perception and output formatting (which have been addressed in subsequent Qwen releases). Applying V-Triune to this specific baseline demonstrates its powerful post-training capability to unlock the model's inherent potential and significantly elevate its performance by refining and amplifying existing strengths.
Consequently, the core usage of Orsta-32B-0321, particularly regarding input formatting and model interaction, largely follows the established patterns of the Qwen2.5-VL series. Users familiar with Qwen2.5-VL models should find the interface intuitive.
For comprehensive details on the general capabilities of Qwen2.5-VL models, including multi-turn dialogue format and image input specifics, we recommend referring to the official
Qwen2.5-VL series documentation (please ensure to consult information relevant to the 32B Instruct version).
Citation 🏆
If you use Orsta-32B-0321 or the V-Triune system in your research, please cite our work:
1@article{ma2025one,
2 title={One RL to See Them All: Visual Triple Unified Reinforcement Learning},
3 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},
4 journal={arXiv preprint arXiv:2505.18129},
5 year={2025}
6}
