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README
License: apache-2.0Results
Across 12 diverse multimodal benchmarks, ContextRL improves over the standard GRPO baseline by +2.0 points on average, while improving every individual benchmark.
| Benchmark | Base | RL (GRPO) | ContextRL (Ours) |
|---|---|---|---|
| MathVista | 68.2 | 72.5 | 73.6 |
| MathVerse | 43.9 | 45.3 | 49.1 |
| MathVision | 22.8 | 25.5 | 26.8 |
| MMMU-Pro | 36.6 | 41.3 | 42.8 |
| MMMU | 50.7 | 53.3 | 54.6 |
| V* | 70.1 | 70.7 | 73.3 |
| MMStar | 62.6 | 64.1 | 65.1 |
| BLINK | 55.3 | 56.5 | 58.9 |
| ScienceQA | 88.2 | 91.0 | 95.4 |
| PhyX | 25.4 | 48.7 | 50.0 |
| OlympiadBench Phy | 1.5 | 3.1 | 4.6 |
| MME-RealWorld Lite | 38.4 | 45.1 | 46.7 |
| Overall Avg. | 47.0 | 51.4 | 53.4 |
The +2.0 average gain over GRPO also exceeds the +0.8 of PAPO, a method purpose-built for multimodal perception (see the paper for the full comparison).
Usage
This model follows the same interface as Qwen2.5-VL-7B-Instruct and can be loaded with
transformers. Training and evaluation code, data construction pipelines, and detailed
configurations are available in the repository:
👉 https://github.com/xupy2003/ContextAwareRL
Please refer to the repo's README for environment setup, inference scripts, and
reproduction instructions.
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xupy21
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Qwen/Qwen2.5-VL-7B-Instruct
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