Update README.md

README.md

@@ -41,8 +41,9 @@ This pipeline can be broken up into three key steps:
 
 2. **Reward Model (RM) fine-tuning:** See [here](https://huggingface.co/FSALab/fsalab-chat-opt-350m-reward-deepspeed) 
 
-3. **Reinforcement-learning from Human feedback (RLHF) fine-tuning:** At the completion of the prior two steps, the final RLHF fine-tuning can be initiated. This involves the collection of both the *fine-tuned model* from step 1 and the *reward model** from step 2 and train them on the data-set with comparisons. This generates both an [actor](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-rlhf-actor-deepspeed) and **critic** model.
+3. **Reinforcement-learning from Human feedback (RLHF) fine-tuning:** At the completion of the prior two steps, the final RLHF fine-tuning can be initiated. This involves the collection of both the *fine-tuned model* from step 1 and the *reward model** from step 2 and train them on the data-set with comparisons. This generates both an [actor](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-rlhf-actor-deepspeed) and **critic** model. I also generate an *[actor model](https://huggingface.co/FSALab/chat-opt-1.3b-rlhf-actor-ema-deepspeed) with an exponential moving average (EMA)* which is known to improve conversational response quality.
 
+   
 To view the details behind each step head into their respective links and view the model card there.
   
 ### Reinforcement learning from human feedback