docs: Revise news section for nemotron v3 and DAPO algorithm support (#1640)

Signed-off-by: Wenwen Gao <94138584+snowmanwwg@users.noreply.github.com>

Author: snowmanwwg

README.md

@@ -10,20 +10,20 @@
 </div>
 
 ## 📣 News
+* [12/15/2025] NeMo-RL is the framework that trained [NVIDIA-NeMotron-3-Nano-30B-A3B-FP8](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8)! [Reproducible code here](https://github.com/NVIDIA-NeMo/RL/tree/nano-v3)
 * [12/1/2025] [Release v0.4.0!](https://github.com/NVIDIA-NeMo/RL/releases/tag/v0.4.0)
     * First release with official NGC Container [nvcr.io/nvidia/nemo-rl:v0.4.0](https://registry.ngc.nvidia.com/orgs/nvidia/containers/nemo-rl/tags).
     * 📊 View the release run metrics on [Google Colab](https://colab.research.google.com/drive/1u5lmjHOsYpJqXaeYstjw7Qbzvbo67U0v?usp=sharing) to get a head start on your experimentation.
 * [10/10/2025] **DAPO Algorithm Support**  
-  NeMo RL now supports [Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO)](https://arxiv.org/pdf/2503.14476) algorithm.  
-  DAPO extends GRPO with **Clip-Higher**, **Dynamic Sampling**, **Token-Level Policy Gradient Loss**, and **Overlong Reward Shaping** for more stable and efficient RL training. See the [DAPO guide](docs/guides/dapo.md) for more details.
-* [9/30/2025] [Accelerated RL on GCP with NeMo RL!](https://discuss.google.dev/t/accelerating-reinforcement-learning-on-google-cloud-using-nvidia-nemo-rl/269579/4) 
+  NeMo RL now supports [Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO)](https://arxiv.org/pdf/2503.14476) algorithm that extends GRPO with **Clip-Higher**, **Dynamic Sampling**, **Token-Level Policy Gradient Loss**, and **Overlong Reward Shaping** for more stable and efficient RL training. See the [DAPO guide](docs/guides/dapo.md) for more details.
 * [9/27/2025] [FP8 Quantization in NeMo RL](https://github.com/NVIDIA-NeMo/RL/discussions/1216)
 * [9/25/2025] On-policy Distillation 
     * Student generates on-policy sequences and aligns logits to a larger teacher via KL, achieving near-larger-model quality at lower cost than RL. See [On-policy Distillation](#on-policy-distillation).
 
 <details>
 <summary>Previous News</summary>
 
+* [9/30/2025] [Accelerated RL on GCP with NeMo RL!](https://discuss.google.dev/t/accelerating-reinforcement-learning-on-google-cloud-using-nvidia-nemo-rl/269579/4) 
 * [8/15/2025] [NeMo-RL: Journey of Optimizing Weight Transfer in Large MoE Models by 10x](https://github.com/NVIDIA-NeMo/RL/discussions/1189)
 * [7/31/2025] [NeMo-RL V0.3: Scalable and Performant Post-training with Nemo-RL via Megatron-Core](https://github.com/NVIDIA-NeMo/RL/discussions/1161)
 * [7/25/2025] [Release v0.3.0!](https://github.com/NVIDIA-NeMo/RL/releases/tag/v0.3.0)