Update README.md

README.md

@@ -35,7 +35,7 @@ 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](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-rlhf-actor-deepspeed) and **actor exponential moving average (EMA)** 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 an [actor](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-rlhf-actor-deepspeed), [critic](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-rlhf-actor-deepspeed) and **actor exponential moving average (EMA)** models.
 
  To view the details behind each step head into their respective links and view the model card there.