diff --git a/README.md b/README.md
index f2d5509d8..d56b5403a 100644
--- a/README.md
+++ b/README.md
@@ -161,7 +161,7 @@ python -m llava.serve.cli \
 
 *Below is the latest training configuration for LLaVA v1.5. For legacy models, please refer to README of [this](https://github.com/haotian-liu/LLaVA/tree/v1.0.1) version for now. We'll add them in a separate doc later.*
 
-LLaVA training consists of two stages: (1) feature alignment stage: use approximately 600K filtered CC3M to connect a *frozen pretrained* vision encoder to a *frozen LLM*; (2) visual instruction tuning stage: use 150K GPT-generated multimodal instruction-following data (with VQA data from academic-oriented tasks) to teach the model to follow multimodal instructions.
+LLaVA training consists of two stages: (1) feature alignment stage: use our 558K subset of the LAION-CC-SBU dataset to connect a *frozen pretrained* vision encoder to a *frozen LLM*; (2) visual instruction tuning stage: use 150K GPT-generated multimodal instruction-following data (with VQA data from academic-oriented tasks) to teach the model to follow multimodal instructions.
 
 LLaVA is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly. Always keep the global batch size the same: `per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`.