RLHF Burned $50K Before We Admitted SFT Would've Worked
Reinforcement Learning from Human Feedback (RLHF) has become the default answer for aligning language models. Everyone wants their GPT-4 moment. But here's what three production deployments taught me: supervised fine-tuning (SFT) beats RLHF on cost, iteration speed, and final performance more often than the research papers let on.
The hype around RLHF comes from its theoretical elegance. You collect preference data, train a reward model, then use PPO to optimize your language model against that reward. It's the same pipeline that gave us ChatGPT. But the gap between "this worked at OpenAI" and "this will work for your 7B parameter model on customer support data" is massive.
Let me show you where RLHF falls apart in practice, and when you should just use supervised learning instead.
The RLHF Tax: 4x Compute, 10x Complexity
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