feat: updated links

README.md

@@ -23,15 +23,21 @@ This model follows the blog of ChatGPT and the paper of InstructGPT and especial
 
 ## Our Training Methodology and Speedup Recipes
 
-This training process is broken up into three key steps:
+The training process simply involves a single python run of DeepSpeed-Chat which initiates the whole 3-step pipeline, saving all models in the process:
+
+``` bash
+python train.py --actor-model facebook/opt-1.3b --reward-model facebook/opt-350m --deployment-type single_node
+```
+
+This pipeline can be broken up into three key steps:
 
 1. **Supervised fine-tuning (SFT):** See [here](https://huggingface.co/FSALab/fsalab-chat-opt-1.3b-sft-deepspeed)
 
 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. 
+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).
 
-To view the details behind each step head into their respective links and view the model card there.
+ To view the details behind each step head into their respective links and view the model card there.
   
 ### Reinforcement learning from human feedback