Where is the real treasure in post-training LLMs?

💡 Where is the real treasure in post-training LLMs? Supervised Fine-Tuning or Reinforcement Learning?

Recently, Reinforcement Learning (especially PPO / GRPO) has gained momentum as a method to fine-tune large language models.

But is this just hype — or does RL truly offer a smarter, more stable way to optimize model behavior without sacrificing generalization?

🧠 I summarized insights from a recent DeepLearning.AI course that explored the three major post-training approaches:

Supervised Fine-Tuning (SFT)

Online Reinforcement Learning (RL – PPO, GRPO)

Direct Preference Optimization (DPO)

🔍 What makes RL different?
SFT: Imitates external examples → may pull the model toward unnatural behavior.

RL: Allows the model to explore and self-correct using reward feedback.

This keeps the model aligned while preserving performance on unrelated tasks.

📚 I highly recommend the course for anyone looking to deeply understand LLM alignment strategies.
👉 Here’s the original walkthrough → DeepLearning.AI course

👇 Check out the breakdown & let me know your take.
Which method do you think will define the future of fine-tuning?

#LLM #RLHF #SFT #DPO #MachineLearning #DeepLearning #AItraining #ReinforcementLearning #PPO #GRPO #OpenAI #Anthropic #PostTraining #Transformers #NLP