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A curated list of papers on reinforcement learning for video generation
| Date | Stars |
|---|---|
| 2026-07-24 | 575 |
| 2026-07-25 | 575 |
| 2026-07-28 | 574 |
| 2026-07-30 | 574 |
| 2026-07-31 | 575 |
| 2026-08-03 | 577 |
| 2026-08-05 | 578 |
| 2026-08-06 | 578 |
Today
— stars today
This week
+4 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.70%/day
# Awesome-RL-for-Video-Generation
## 🤖 Introduction
Welcome to the GitHub repository for **Awesome-RL-for-Video-Generation**! This repository serves as a curated collection of research, resources, and tools related to **Reinforcement Learning (RL) for Video Generation**. Our goal is to provide an up-to-date and comprehensive overview of RL techniques used in video generation, focusing on the latest advancements. We aim to bridge the gap between RL theory and real-world applications in video generation tasks, offering a solid foundation for future research and development in this field. We hope this repository will serve as a valuable resource for anyone interested in exploring RL applications in video generation!
## 🔥 News
- **[February 14, 2025]** We have developed an agent that automatically collects and analyzes the latest papers in the RL-based Video Generation field. It will update the [Related Papers](#-related-papers) daily at 1:00 AM UTC+8.
## 🔍 Related Papers
We are committed to offering researchers the latest advancements in the field. By regularly reviewing and evaluating recent research studies, we ensure that the list of papers stays up-to-date.
⚠️ **The paper analysis may not be accurate and is for reference only!**
<table style="width: 100%;">
<tr>
<td><strong>Date</strong></td>
<td><strong>Paper</strong></td>
<td><strong>Contribution</strong></td>
<td><strong>Available Link</strong></td>
</tr>
<tr>
<td rowspan="2" style="width: 15%;">Apr 2026</td>
<td style="width: 70%;"><strong>ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception</strong></td>
<td style="width: 15%;">
<a><img src="https://img.shields.io/badge/Method-blue"></a><br>
</td>
<td style="width: 15%;">
<a href="http://arxiv.org/pdf/2604.12255"><img src="https://img.shields.io/badge/Paper-red"></a><br>
</td>
</tr>
<tr>
<td colspan="4">
• Affiliation: Fudan University<br>
• Method Name: ARGen, Base Model: Qwen2.5-7B-VL, Strategy: policy gradient method<br>
</td>
</tr>
<tr>
<td rowspan="2" style="width: 15%;">Apr 2026</td>
<td style="width: 70%;"><strong>ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception</strong></td>
<td style="width: 15%;">
<a><img src="https://img.shields.io/badge/Method-blue"></a><br>
</td>
<td style="width: 15%;">
<a href="http://arxiv.org/pdf/2604.12255"><img src="https://img.shields.io/badge/Paper-red"></a><br>
</td>
</tr>
<tr>
<td colspan="4">
• Affiliation: Fudan University<br>
• Method Name: ARGen, Base Model: ModelScopeT2V, Strategy: Policy Gradient<br>
</td>
</tr>
<tr>
<td rowspan="2" style="width: 15%;">Apr 2026</td>
<td style="width: 70%;"><strong>LPM 1.0: Video-based Character Performance Model</strong></td>
<td style="width: 15%;">
<a><img src="https://img.shields.io/badge/Method-blue"></a><br>
<a><img src="https://img.shields.io/badge/Benchmark-blue"></a><br>
</td>
<td style="width: 15%;">
<a href="http://arxiv.org/pdf/2604.07823"><img src="https://img.shields.io/badge/Paper-red"></a><br>
<a href="https://large-performance-model.github.io"><img src="https://img.shields.io/badge/Website-9cf"></a><br>
</td>
</tr>
<tr>
<td colspan="4">
• Affiliation: International Digital Economy Academy<br>
• Method Name: LPM 1.0, Base Model: Wan2.1-I2V (16B), Strategy: DPO<br>
• Benchmark Name: LPM-Bench, Data Number: 1000, Evaluation Metric: Motion Dynamics, Identity Consistency, Text Controllability, Audio-Video Synchronization<br>
</td>
</tr>
<tr>
<td rowspan="2" style="width: 15%;">Apr 2026</td>
<td style="width: 70%;"><strong>LPM 1.0: Video-based Character Performance Model</strong></td>
<td style="width: 15%;">
<a><img src="https://img.shields.io/badge/Method-blue"></a><br>
<a><img src="https://imExcerpt of 214,046 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d89aa3ae1f2992b5, topic:video-generation, name:video generation, desc:video generation
matched fp:d89aa3ae1f2992b5, topic:dpo, readme:dpo
matched fp:d89aa3ae1f2992b5, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning