tongruiliu

tongruiliu / Guided-GRPO

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A Guided Reinforcement Learning framework enhancing MLLM reasoning via process-level verification and collaborative rollout strategies.

42
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100% credibility
Found Feb 06, 2026 at 27 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Guided-GRPO is a framework that trains multimodal AI models using guided reinforcement learning with verifier feedback to stabilize reasoning and reduce errors.

How It Works

1
🔍 Discover Guided-GRPO

You hear about a helpful tool that trains AI to reason smartly with pictures and videos, fixing mistakes as it learns.

2
📥 Get it ready

Download the tool and set it up on your computer with a simple command, like installing any helpful app.

3
📚 Add your examples

Gather simple question-answer pairs with images or videos, like teaching examples from everyday puzzles.

4
🤖 Pick your AIs

Choose a guiding teacher AI and a learning student AI to work together on your examples.

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▶️ Start the magic

Hit go, and watch the student AI practice reasoning step-by-step while the teacher gently corrects errors.

6
📈 Watch it improve

See your AI get better at handling visuals, with fewer slip-ups over time.

✅ Smart AI ready

Your AI now confidently solves visual reasoning tasks, ready for real-world use!

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AI-Generated Review

What is Guided-GRPO?

Guided-GRPO is a Python framework for training multimodal large language models (MLLMs) using guided reinforcement learning, where a lightweight verifier provides step-by-step corrections during rollouts to stabilize training and curb error propagation. It transforms open-loop generation into closed-loop reasoning by injecting process-level feedback, turning sparse rewards into dense signals for algorithms like GRPO, DAPO, and GSPO. Users get scalable distributed training via Ray and vLLM, with support for Qwen2-VL and similar vision-language models on custom datasets.

Why is it gaining traction?

It stands out by enabling verifier-guided rollouts—either local or HTTP-based—for multimodal tasks, with quickstarts like running GRPO in one command or multi-turn guided sessions via YAML configs. Docker support and examples for reward functions make experimentation fast, while FSDP handles large models efficiently. Developers appreciate the focus on guided reinforcement policy optimization without needing a value network.

Who should use this?

AI researchers fine-tuning MLLMs like Qwen-VL on reasoning benchmarks with images/videos, especially those battling instability in standard RLHF. It's ideal for teams exploring guided reinforcement learning for robust multi-contact loco-manipulation or task automation, where process verification boosts sample efficiency.

Verdict

Try it for guided reinforcement learning prototypes if you have GPUs and Qwen familiarity—docs and examples are solid, backed by a fresh arXiv paper. With just 30 stars and 1.0% credibility, it's early-stage; expect tweaks for production.

(198 words)

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