Update README.md

README.md

@@ -87,8 +87,13 @@ We enhance the model's contextual understanding using image-based question-answe
 **Direct Preference Optimization (DPO):**
 The final stage implements DPO by first generating responses to images using the base model. A teacher model then produces minimally edited corrections while maintaining high semantic similarity with the original responses, focusing specifically on accuracy-critical elements. These original and corrected outputs form chosen-rejected pairs. The fine-tuning targeted at essential model output improvements without altering the model's core response characteristics
 
-## What's next?
-We are continually improving Omnivision for better on-device performance. Stay tuned.
+## What's next for Omnivision?
+Omnivision is in early development and we are working to address current limitations:
+- Expand DPO Training: Increase the scope of DPO (Direct Preference Optimization) training in an iterative process to continually improve model performance and response quality.
+- Develop an Action + Conversation Model: Leverage Omnivision’s vision and conversational capacities to build an action model capable of understanding and interacting with visual and text inputs.
+- Improve document and text understanding
+  
+In the long term, we aim to develop Omnivision as a fully optimized, production-ready solution for edge AI multimodal applications.
 
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