diff --git a/docs/user-guide/SFT.rst b/docs/user-guide/SFT.rst
index a1a5fbe95..0d5fb3d98 100644
--- a/docs/user-guide/SFT.rst
+++ b/docs/user-guide/SFT.rst
@@ -186,8 +186,8 @@ Now that we have the data we will use NeMo-Aligner to do the supervised fine tun
                trainer.devices=8 \
                trainer.sft.max_steps=-1 \
                trainer.sft.limit_val_batches=40 \
-               trainer.sft.val_check_interval=1000 \
-               trainer.sft.save_interval=50 \
+               trainer.sft.val_check_interval=100 \
+               trainer.sft.save_interval=100 \
                model.megatron_amp_O2=True \
                model.restore_from_path=${PRETRAINED_ACTOR_NEMO_FILE} \
                model.optim.lr=5e-6 \
diff --git a/docs/user-guide/SteerLM.rst b/docs/user-guide/SteerLM.rst
index 1f83607c7..635abe5a3 100644
--- a/docs/user-guide/SteerLM.rst
+++ b/docs/user-guide/SteerLM.rst
@@ -126,6 +126,7 @@ Note that you would need to set up multi-node training in your cluster env, depe
          pretrained_checkpoint.restore_from_path=/models/llama13b/llama13b.nemo \
          "model.data.data_prefix={train: ["data/merge_train_reg.jsonl"], validation: ["data/merge_val_reg.jsonl"], test: ["data/merge_val_reg.jsonl"]}" \
          exp_manager.explicit_log_dir=/results/reward_model_13b \
+         trainer.rm.save_interval=100 \
          trainer.rm.val_check_interval=10 \
          exp_manager.create_wandb_logger=True \
          exp_manager.wandb_logger_kwargs.project=steerlm \