diff --git a/docs/source/nlp/nemo_megatron/intro.rst b/docs/source/nlp/nemo_megatron/intro.rst
index fab448f3d4f2..74d8510fd25e 100644
--- a/docs/source/nlp/nemo_megatron/intro.rst
+++ b/docs/source/nlp/nemo_megatron/intro.rst
@@ -20,6 +20,7 @@ To learn more about using NeMo to train Large Language Models at scale, please r
    peft/landing_page
    positional_embeddings
    mcore_customization
+   reset_learning_rate
 
 
 References
@@ -28,4 +29,4 @@ References
 .. bibliography:: ../nlp_all.bib
     :style: plain
     :labelprefix: nlp-megatron
-    :keyprefix: nlp-megatron-
\ No newline at end of file
+    :keyprefix: nlp-megatron-
diff --git a/docs/source/nlp/nemo_megatron/reset_learning_rate.rst b/docs/source/nlp/nemo_megatron/reset_learning_rate.rst
new file mode 100644
index 000000000000..f89daeeb3907
--- /dev/null
+++ b/docs/source/nlp/nemo_megatron/reset_learning_rate.rst
@@ -0,0 +1,30 @@
+.. _reset_learning_rate:
+
+Reset Learning Rate
+-------------------
+
+The reset learning rate feature provides the ability to reset the learning rate for an existing checkpoint to its initial value (either 0 or ``optim.min_lr`` depending on the warmup steps) when performing continual pretraining.
+
+Parameters
+----------
+
+* ``reset_lr`` (boolean): Enables resetting the learning rate to the initial value. This feature is only supported with the distributed optimizer and megatron_amp_O2.
+* ``reset_lr_steps`` (boolean): Enables adjusting the learning rate's max_steps and decay_steps by subtracting the number of steps already completed at the checkpoint.
+
+Use Cases
+---------
+
+1. ``reset_lr=True, reset_lr_steps=False``
+When pretraining an existing checkpoint "from scratch" on a different dataset. The learning rate will be reset to its initial value. This allows the model to start training on a new dataset with the same learning rate dynamics as if it were starting from scratch.
+
+2. ``reset_lr=True, reset_lr_steps=True``
+When continuing training from an existing checkpoint with the same configuration. The learning rate will be reset to its initial value, and the ``max_steps`` and ``decay_steps`` for learning rate schedule will be recalculated by subtracting the number of steps already completed at the checkpoint. Specifically:
+    * ``max_steps`` will be recalculated as ``max_steps -= completed_steps``.
+    * ``decay_steps`` will be recalculated as ``decay_steps -= completed_steps``.
+This ensures that the learning rate reaches the ``min_lr`` value by the end of training without changing the ``trainer.max_steps``:
+
+.. image:: https://github.com/NVIDIA/NeMo/releases/download/v2.0.0rc0/asset-post-reset-learning-rate-example.png
+  :alt: 
+  :width: 1080px
+
+