docs: improve Megatron-LM and Megatron Core descriptions

README.md

@@ -13,14 +13,13 @@ Megatron-LM and Megatron Core
 
 ## About
 
-**Megatron-Core (MCore)**: Composable library with GPU-optimized building blocks for custom training frameworks.
-You can install this library using pip or use it within the Megatron-LM GitHub repository.
+This repository contains two components: **Megatron-LM** and **Megatron Core**.
 
-**Megatron-LM**: Reference implementation that includes end-to-end examples utilizing Megatron Core.
+**Megatron-LM** is a reference example that includes Megatron Core plus pre-configured training scripts. Best for research teams, learning distributed training, and quick experimentation.
 
-**Megatron-Bridge**: Training library with bidirectional Hugging Face ↔ Megatron checkpoint conversion, flexible training loops, and example model training recipes.
+**Megatron Core** is a composable library with GPU-optimized building blocks for custom training frameworks. It provides transformer building blocks, advanced parallelism strategies (TP, PP, DP, EP, CP), mixed precision support (FP16, BF16, FP8, FP4), and model architectures. Best for framework developers and ML engineers building custom training pipelines.
 
-For more information, refer to [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge).
+**[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** provides bidirectional Hugging Face ↔ Megatron checkpoint conversion with production-ready recipes.
 
 
 ## Quick Start
@@ -44,7 +43,8 @@ Install Megatron Core with pip:
 
 # Latest News
 
-- **[2025/12]** 🎉 **Megatron Core development has moved to GitHub!** All development and CI now happens in the open. We welcome community contributions.
+- **[2026/01]** **[Dynamic Context Parallelism](https://developer.nvidia.com/blog/speeding-up-variable-length-training-with-dynamic-context-parallelism-and-nvidia-megatron-core/)** - Up to 1.48x speedup for variable-length sequence training with adaptive CP sizing.
+- **[2025/12]** **Megatron Core development has moved to GitHub!** All development and CI now happens in the open. We welcome community contributions.
 - **[2025/10]** **[Megatron Dev Branch](https://github.com/NVIDIA/Megatron-LM/tree/dev)** - early access branch with experimental features.
 - **[2025/10]** **[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** - Bidirectional converter for interoperability between Hugging Face and Megatron checkpoints, featuring production-ready recipes for popular models.
 - **[2025/08]** **[MoE Q3-Q4 2025 Roadmap](https://github.com/NVIDIA/Megatron-LM/issues/1729)** - Comprehensive roadmap for MoE features including DeepSeek-V3, Qwen3, advanced parallelism strategies, FP8 optimizations, and Blackwell performance enhancements.
@@ -57,7 +57,7 @@ Install Megatron Core with pip:
 
 - **[2024/07]** Megatron Core v0.7 improves scalability and training resiliency and adds support for multimodal training ([blog](https://developer.nvidia.com/blog/train-generative-ai-models-more-efficiently-with-new-nvidia-Megatron-Core-functionalities/)).
 - **[2024/06]** Megatron Core added supports for Mamba-based models. Check out our paper [An Empirical Study of Mamba-based Language Models](https://arxiv.org/pdf/2406.07887) and [code example](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba).
-- **[2024/01 Announcement]** NVIDIA has released the core capabilities in **Megatron-LM** into [**Megatron Core**](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) in this repository. Megatron Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs. Explore the [Megatron Core intro](#Megatron Core) for more details.
+- **[2024/01 Announcement]** NVIDIA has released the core capabilities in **Megatron-LM** into [**Megatron Core**](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) in this repository. Megatron Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs.
 
 </details>
 
@@ -93,7 +93,7 @@ Megatron-LM/
 
 # Performance Benchmarking
 
-For our latest performance benchmarking results, please refer to [NVIDIA NeMo Framework Performance Summary](https://docs.nvidia.com/nemo/megatron-bridge/latest/performance-summary.html).
+For our latest performance benchmarking results, please refer to [NVIDIA Megatron Bridge Performance Summary](https://docs.nvidia.com/nemo/megatron-bridge/latest/performance-summary.html).
 
 Our codebase efficiently trains models from 2B to 462B parameters across thousands of GPUs, achieving up to **47% Model FLOP Utilization (MFU)** on H100 clusters.
 
@@ -131,6 +131,10 @@ We also strong scaled the standard GPT-3 model (our version has slightly more th
 
 
 
+# Roadmaps
+
+- **[MoE Roadmap](https://github.com/NVIDIA/Megatron-LM/issues/1729)** - DeepSeek-V3, Qwen3, advanced parallelism, FP8 optimizations, and Blackwell enhancements
+
 # Resources
 
 ## Getting Help