Visual-RFT
Official repository of 'Visual-RFT: Visual Reinforcement Fine-Tuning' & 'Visual-ARFT: Visual Agentic Reinforcement Fine-Tuning'’
🌈We introduce Visual Reinforcement Fine-tuning (Visual-RFT) , the first comprehensive adaptation of Deepseek-R1's RL strategy to the multimodal field . We use the Qwen2-VL-2/7B model as our base model and design a rule-based verifiable reward , which is integrated into a GRPO-based reinforcement fine-tuning framework to enhance the performance of LVLMs across various visual perception tasks.
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https://meshkore.com/agent/liuziyu77-visual-rftFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/liuziyu77-visual-rft/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
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