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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Survey on Reinforcement Learning of Vision-Language-Action Models for Robotic Manipulation
| Date | Stars |
|---|---|
| 2026-07-31 | 812 |
| 2026-08-01 | 811 |
| 2026-08-02 | 811 |
| 2026-08-03 | 812 |
| 2026-08-04 | 814 |
| 2026-08-05 | 815 |
| 2026-08-06 | 815 |
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# Awesome RL-VLA for Robotic Manipulation 🤖
**[[Paper](https://www.techrxiv.org/users/934012/articles/1366553-a-survey-on-reinforcement-learning-of-vision-language-action-models-for-robotic-manipulation?commit=a7c4cf9ff94956e6d3c7f6eb1ff10a6eb2fc05e4)]**
A curated list of papers and resources on **Reinforcement Learning of Vision-Language-Action (RL-VLA)** models for Robotic Manipulation. This repository provides a comprehensive overview of training paradigms, methodologies, and state-of-the-art approaches in RL-VLA research.
## 📢 Latest News
> 🔥 **[November 2025]** Our comprehensive survey paper **"A Survey on Reinforcement Learning of Vision-Language-Action Models for Robotic Manipulation"** is now available on [TechRxiv](https://doi.org/10.36227/techrxiv.176531955.54563920/v1)! Stay tuned for future updates.
>
## 📖 Table of Contents
- [Awesome RL-VLA for Robotic Manipulation 🤖](#awesome-rl-vla-for-robotic-manipulation-)
- [📢 Latest News](#-latest-news)
- [📖 Table of Contents](#-table-of-contents)
- [🔍 Overview](#-overview)
- [🚀 Training Paradigms](#-training-paradigms)
- [Offline RL-VLA](#offline-rl-vla)
- [Online RL-VLA](#online-rl-vla)
- [Test-time RL-VLA](#test-time-rl-vla)
- [📚 Paper Collection](#-paper-collection)
- [Legend](#legend)
- [Offline RL-VLA](#offline-rl-vla-1)
- [Online RL-VLA](#online-rl-vla-1)
- [Offline + Online RL-VLA](#offline--online-rl-vla)
- [Test-time RL-VLA](#test-time-rl-vla-1)
- [🔗 Useful Resources](#-useful-resources)
- [🎯 RL-VLA Action Optimization](#-rl-vla-action-optimization)
- [Base VLA Models](#base-vla-models)
- [Datasets \& Benchmarks](#datasets--benchmarks)
- [Frameworks \& Tools](#frameworks--tools)
- [🤝 Contributing](#-contributing)
- [Contribution Guidelines](#contribution-guidelines)
- [📄 Citation](#-citation)
- [⭐ Star History](#-star-history)
## 🔍 Overview
RL training is crucial for enabling VLAs to generalize out-of-distribution (OOD) from large-scale pre-trained data. Existing RL-VLA training paradigms can be categorized into three types based on how agents obtain and utilize feedback from the environment:
- **Online RL-VLA**: Direct interaction with the environment during training
- **Offline RL-VLA**: Learning from static datasets without further environmental interaction
- **Test-time RL-VLA**: Models adapt their behavior during deployment without altering parameters
## 🚀 Training Paradigms
### Offline RL-VLA
Offline RL trains VLA models on pre-collected static datasets, enabling learning independently from environment interactions. This paradigm is suitable for high-risk or resource-constrained deployment scenarios.
**Key Research Directions:**
- **Data Utilization**: Effective utilization of static datasets for policy improvement
- **Objective Modification**: Customizing RL objectives for novel architectures and data augmentation
### Online RL-VLA
Online RL-VLA enables interactive policy learning through continuous environment interaction, empowering pre-trained VLAs with adaptive closed-loop control capability for real-world OOD environments.
**Key Research Directions:**
- **Policy Optimization**: Direct policy improvement based on environmental rewards
- **Sample Efficiency**: Learning effective policies with limited interaction budget
- **Active Exploration**: Efficient exploration strategies for higher performance gains
- **Training Stability**: Ensuring consistent policy updates and convergence
- **Infrastructure**: Scalable frameworks for online RL-VLA training
### Test-time RL-VLA
Test-time RL-VLA adapts behavior during deployment through lightweight updates, addressing the expensive cost of full model fine-tuning in real-world scenarios.
**Key Adaptation Mechanisms:**
- **Value Guidance**: Using pre-trained value functions to influence action selection
- **Memory Buffer Guidance**: Retrieving relevant historical experiences during inference
- **Planning-guided AExcerpt of 25,263 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7a5c35a3b9bb2530, desc:manipulation, desc:vision-language-action