diff --git a/.github/workflows/gpu_test.yaml b/.github/workflows/gpu_test.yaml
index ec9e33116..c2a4705e5 100644
--- a/.github/workflows/gpu_test.yaml
+++ b/.github/workflows/gpu_test.yaml
@@ -1,4 +1,4 @@
-name: GPU tests
+name: GPU Tests
 
 on:
   schedule:
diff --git a/.github/workflows/unit_test.yaml b/.github/workflows/unit_test.yaml
index 28a30fdc9..fb5ae675b 100644
--- a/.github/workflows/unit_test.yaml
+++ b/.github/workflows/unit_test.yaml
@@ -1,4 +1,4 @@
-name: Unit Test
+name: Unit Tests
 
 on:
   pull_request:
diff --git a/README.md b/README.md
index be993a2b9..c9d2a0d2b 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,8 @@
 # <img width="35" height="35" alt="image" src="https://github.com/user-attachments/assets/2700a971-e5d6-4036-b03f-2f89c9791609" /> Forge
 
-
 #### A PyTorch-native agentic RL library that lets you focus on algorithms—not infra.
+[![Unit Tests](https://github.com/meta-pytorch/forge/actions/workflows/unit_test.yaml/badge.svg)](https://github.com/meta-pytorch/forge/actions/workflows/unit_test.yaml)
+[![GPU Tests](https://github.com/meta-pytorch/forge/actions/workflows/gpu_test.yaml/badge.svg)](https://github.com/meta-pytorch/forge/actions/workflows/gpu_test.yaml)
 
 ## Overview
 The primary purpose of the Forge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. Forge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.