Visual-Agent/DeepEyes

Health v2 · maintenance only

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Adoption not part of the score

1271 stars · 78 forks observed · 2026-08-28

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

DeepEyes is a research project that trains multimodal vision-language models to 'think with images' using end-to-end reinforcement learning, built on the VeRL framework with Qwen-2.5-VL as the base model. It includes training code, a 47k dataset, and released 7B model checkpoints.

Use cases

• train a vision-language model with reinforcement learning
• reproduce thinking-with-images RL experiments
• improve visual grounding of a multimodal LLM
• fine-tune Qwen-2.5-VL with outcome-reward RL
• research tool-calling behavior emerging from RL training

When to choose

• you need to RL-train a multimodal model to reason over images
• you want to reproduce the DeepEyes paper results
• you have a multi-node GPU cluster for 7B+ model training

When to avoid

• you just want to run inference with an existing VLM
• you lack large-scale GPU resources (32+ GPUs recommended)
• you need a production-ready application rather than research code

Facets

library · maturity active

reinforcement-learning machine-learning llm-training computer-vision agent-framework artificial-intelligence machine-learning computer-vision large-language-models deep-learning python multimodal vision-language-model rl-training qwen thinking-with-images research gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Visual-Agent/DeepEyes")