add arxiv badge and link to torchtitan paper (pytorch#618)

This PR:
1 - adds an arvix badge underneath the two CI badges that links to the
titan arxiv paper.
2 - adds a two sentence section in the main body summarizing our paper
with link
3 - makes a minor grammar correction regarding using latest nightly.

README.md

@@ -3,7 +3,7 @@
 
 # torchtitan
 
-`torchtitan` is currently in a pre-release state and under extensive development. Currently we showcase pre-training **Llama 3.1**, **Llama 3**, and **Llama 2** LLMs of various sizes from scratch. To use the latest features of `torchtitan`, we recommend latest PyTorch nightly.
+`torchtitan` is currently in a pre-release state and under extensive development. Currently we showcase pre-training **Llama 3.1**, **Llama 3**, and **Llama 2** LLMs of various sizes from scratch. To use the latest features of `torchtitan`, we recommend using the most recent PyTorch nightly.
 
 `torchtitan` is a proof-of-concept for Large-scale LLM training using native PyTorch. It is (and will continue to be) a repo to showcase PyTorch's latest distributed training features in a clean, minimal codebase. torchtitan is complementary to and not a replacement for any of the great large-scale LLM training codebases such as Megatron, Megablocks, LLM Foundry, Deepspeed, etc. Instead, we hope that the features showcased in torchtitan will be adopted by these codebases quickly. torchtitan is unlikely to ever grow a large community around it.
 
@@ -18,6 +18,12 @@ Our guiding principles when building `torchtitan`:
 
 [![Welcome to torchtitan!](assets/images/titan_play_video.png)](https://youtu.be/ee5DOEqD35I?si=_B94PbVv0V5ZnNKE "Welcome to torchtitan!")
 
+### Our torchtitan paper on arXiv
+
+[![arXiv](https://img.shields.io/badge/arXiv-2410.06511-b31b1b.svg?style=plastic)](https://arxiv.org/abs/2410.06511)
+
+We provide a detailed look into the parallelisms and optimizations available in `torchtitan`, along with summary advice on when to use various techniques:  [TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training](https://arxiv.org/abs/2410.06511)
+
 ### Dive into the code
 
 You may want to see how the model is defined or how parallelism techniques are applied. For a guided tour, see these files first: