Liuziyu77/Visual-RFT

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2271 stars · 111 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official research code for Visual-RFT and Visual-ARFT, applying GRPO-based reinforcement fine-tuning with rule-based verifiable rewards to multimodal large vision-language models like Qwen2-VL. It extends Deepseek-R1's RL strategy to visual perception tasks such as open vocabulary detection, few-shot detection, reasoning grounding, and fine-grained image classification.

Use cases

• reinforcement fine-tune a vision-language model with GRPO
• train Qwen2-VL for open vocabulary detection
• improve few-shot object detection with RL rewards
• apply R1-style verifiable rewards to multimodal tasks
• fine-tune LVLMs for reasoning grounding
• train agentic visual models with Visual-ARFT

When to choose

• you want to reproduce or extend Visual-RFT/Visual-ARFT research
• you need GRPO-based RL fine-tuning for Qwen2-VL on perception tasks
• you want rule-based verifiable rewards for detection or classification fine-tuning

When to avoid

• you need a production-ready training framework with broad model support
• you only want inference without fine-tuning
• you use models other than Qwen2-VL without adaptation work

Facets

library · maturity active

machine-learning llm-training computer-vision agent-framework machine-learning computer-vision large-language-models deep-learning python reinforcement-fine-tuning grpo multimodal qwen2-vl visual-perception research-code verifiable-rewards ai-agents gpu linux

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