polish(pu): update latest paper info in readme

Author: puyuan1996

README.md

@@ -28,13 +28,22 @@
 [![GitHub license](https://img.shields.io/github/license/opendilab/LightZero)](https://github.com/opendilab/LightZero/blob/master/LICENSE)
 [![discord badge](https://dcbadge.vercel.app/api/server/dkZS2JF56X?style=flat)](https://discord.gg/dkZS2JF56X)
 
-Updated on 2025.06.03 LightZero-v0.2.0
+Updated on 2025.09.29 LightZero-v0.2.0
 
-English | [简体中文(Simplified Chinese)](https://github.com/opendilab/LightZero/blob/main/README.zh.md) | [Documentation](https://opendilab.github.io/LightZero) | [LightZero Paper](https://arxiv.org/abs/2310.08348) | [🔥UniZero Paper](https://arxiv.org/abs/2406.10667) | [🔥ReZero Paper](https://arxiv.org/abs/2404.16364)
 
 > LightZero is a lightweight, efficient, and easy-to-understand open-source algorithm toolkit that combines Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (RL).
 > For any questions about LightZero, you can consult the RAG-based Q&A assistant: [ZeroPal](https://huggingface.co/spaces/OpenDILabCommunity/ZeroPal).
 
+English | [简体中文(Simplified Chinese)](https://github.com/opendilab/LightZero/blob/main/README.zh.md) | [Documentation](https://opendilab.github.io/LightZero) | [LightZero Paper](https://arxiv.org/abs/2310.08348) | [UniZero Paper](https://arxiv.org/abs/2406.10667) | [ReZero Paper](https://arxiv.org/abs/2404.16364)| [🔥ScaleZero Paper](https://arxiv.org/abs/2509.07945)
+
+## News
+
+- [2025.09] 🔥 ScaleZero 论文已发布在 arXiv: [One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning](https://arxiv.org/abs/2509.07945).
+- [2025.08] [ReZero 论文](https://openreview.net/forum?id=F9Y7j3AJTu)已被 CoRL 2025 RemembeRL workshop 接收。
+- [2024.06] [UniZero 论文](https://openreview.net/forum?id=Gl6dF9soQo)已被 Transactions on Machine Learning Research 接收。
+- [2023.09] [LightZero 论文](https://proceedings.neurips.cc/paper_files/paper/2023/hash/765043fe026f7d704c96cec027f13843-Abstract-Datasets_and_Benchmarks.html)已被 NeurIPS 2023 Datasets and Benchmarks Track 接收为 Spotlight Presentation。
+- [2023.04] LightZero v0.0.1 正式发布。
+- 
 
 ## 🔍 Background
 
@@ -608,11 +617,10 @@ Here is a collection of research papers about **Monte Carlo Tree Search**.
   year={2024}
 }
 
-@article{pu2024unizero,
+@article{puunizero,
   title={UniZero: Generalized and Efficient Planning with Scalable Latent World Models},
-  author={Pu, Yuan and Niu, Yazhe and Ren, Jiyuan and Yang, Zhenjie and Li, Hongsheng and Liu, Yu},
-  journal={arXiv preprint arXiv:2406.10667},
-  year={2024}
+  author={Pu, Yuan and Niu, Yazhe and Yang, Zhenjie and Ren, Jiyuan and Li, Hongsheng and Liu, Yu},
+  journal={Transactions on Machine Learning Research}
 }
 
 @article{xuan2024rezero,
@@ -621,6 +629,13 @@ Here is a collection of research papers about **Monte Carlo Tree Search**.
   journal={arXiv preprint arXiv:2404.16364},
   year={2024}
 }
+
+@article{pu2025one,
+  title={One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning},
+  author={Pu, Yuan and Niu, Yazhe and Tang, Jia and Xiong, Junyu and Hu, Shuai and Li, Hongsheng},
+  journal={arXiv preprint arXiv:2509.07945},
+  year={2025}
+}
 ```
 
 ## 💓 Acknowledgments

README.zh.md

@@ -27,14 +27,22 @@
 [![Contributors](https://img.shields.io/github/contributors/opendilab/LightZero)](https://github.com/opendilab/LightZero/graphs/contributors)
 [![GitHub license](https://img.shields.io/github/license/opendilab/LightZero)](https://github.com/opendilab/LightZero/blob/master/LICENSE)
 
-最近更新于 2025.06.03 LightZero-v0.2.0
-
-[English](https://github.com/opendilab/LightZero/blob/main/README.md) | 简体中文 | [文档](https://opendilab.github.io/LightZero) | [LightZero 论文](https://arxiv.org/abs/2310.08348) | [🔥UniZero 论文](https://arxiv.org/abs/2406.10667) | [🔥ReZero 论文](https://arxiv.org/abs/2404.16364)
+最近更新于 2025.09.29 LightZero-v0.2.0
 
 > LightZero 是一个轻量、高效、易懂的 MCTS+RL 开源算法库。
 > 有关 LightZero 的任何疑问，您都可以咨询基于 RAG 技术的问答助手：[ZeroPal](https://huggingface.co/spaces/OpenDILabCommunity/ZeroPal)。
 
 
+[English](https://github.com/opendilab/LightZero/blob/main/README.md) | 简体中文 | [文档](https://opendilab.github.io/LightZero) | [LightZero 论文](https://arxiv.org/abs/2310.08348) | [UniZero 论文](https://arxiv.org/abs/2406.10667) | [ReZero 论文](https://arxiv.org/abs/2404.16364) | [🔥ScaleZero 论文](https://arxiv.org/abs/2509.07945)
+
+## 研究日志
+- [2025.09] 🔥 ScaleZero 论文已发布在 arXiv: [One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning](https://arxiv.org/abs/2509.07945).
+- [2025.08] [ReZero 论文](https://openreview.net/forum?id=F9Y7j3AJTu)已被 CoRL 2025 RemembeRL workshop 接收。
+- [2024.06] [UniZero 论文](https://openreview.net/forum?id=Gl6dF9soQo)已被 Transactions on Machine Learning Research 接收。
+- [2023.09] [LightZero 论文](https://proceedings.neurips.cc/paper_files/paper/2023/hash/765043fe026f7d704c96cec027f13843-Abstract-Datasets_and_Benchmarks.html)已被 NeurIPS 2023 Datasets and Benchmarks Track 接收为 Spotlight Presentation。
+- [2023.04] LightZero v0.0.1 正式发布。
+
+
 ## 🔍 背景
 
 以 AlphaZero, MuZero 为代表的结合蒙特卡洛树搜索 (Monte Carlo Tree Search, MCTS) 和深度强化学习 (Deep Reinforcemeent Learning, DRL) 的方法，在诸如围棋，Atari 等各种游戏上取得了超人的水平，也在诸如蛋白质结构预测，矩阵乘法算法寻找等科学领域取得了可喜的进展。下图为蒙特卡洛树搜索（MCTS）算法族的发展历史：
@@ -53,6 +61,7 @@
 
 ### 导航
 - [LightZero](#lightzero)
+  - [研究日志](#研究日志)
   - [🔍 背景](#-背景)
   - [🎨 概览](#-概览)
     - [导航](#导航)
@@ -592,11 +601,10 @@ and internal state transition dynamics,
   year={2024}
 }
 
-@article{pu2024unizero,
+@article{puunizero,
   title={UniZero: Generalized and Efficient Planning with Scalable Latent World Models},
-  author={Pu, Yuan and Niu, Yazhe and Ren, Jiyuan and Yang, Zhenjie and Li, Hongsheng and Liu, Yu},
-  journal={arXiv preprint arXiv:2406.10667},
-  year={2024}
+  author={Pu, Yuan and Niu, Yazhe and Yang, Zhenjie and Ren, Jiyuan and Li, Hongsheng and Liu, Yu},
+  journal={Transactions on Machine Learning Research}
 }
 
 @article{xuan2024rezero,
@@ -605,6 +613,13 @@ and internal state transition dynamics,
   journal={arXiv preprint arXiv:2404.16364},
   year={2024}
 }
+
+@article{pu2025one,
+  title={One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning},
+  author={Pu, Yuan and Niu, Yazhe and Tang, Jia and Xiong, Junyu and Hu, Shuai and Li, Hongsheng},
+  journal={arXiv preprint arXiv:2509.07945},
+  year={2025}
+}
 ```
 
 ## 💓 致谢