When I worked as an AI trainer 💻 at Soul AI, one of the challenging topics ⚠ I encountered was Reinforcement Learning from Human Feedback (RLHF). I came across an analogy 💯 that helped me understand it more clearly, and I want to share that with you 👨💻.
Imagine teaching a child 👶 to speak 🗣 and think 🗯. You'd start by introducing them to basic language 🈹 🈲 through stories 🌃 and conversations 🗺, correcting them gently 🤝, and encouraging their curiosity 👌 . As they grow 👧 , you'd offer more complex ideas 🕵♀️ and nuanced feedback 🎯 , guiding them to understand not just words, but also the values ⛳ and context ♥ behind them.
This is quite similar 👯♀️ to Reinforcement Learning from Human Feedback (RLHF) 🤖 in the AI world 👨💻 . Mainly RLHF contains:
▶ Supervised Fine-Tuning (SFT): Think of this as the initial schooling phase 🏫 for an AI. Just as a child learns basic language skills from books 📖 and conversations 👥 , the AI is fed a vast array of text 🔠 . This builds its foundational 🥅 understanding of language 🔤 .
▶ Reward Modeling: Here, AI trainers act like attentive parents 👨⚖️ or teachers 👩💼 , evaluating the AI's 'responses' or 'answers'. They rate ✔ these responses to teach the AI what a good answer looks like. It's similar to a parent praising 👌 a child for a thoughtful answer or gently correcting ❌ them when they're off-mark 🏸.
▶ Proximal Policy Optimization (PPO): This is where the AI starts 'practicing' 🏋♂️ on its own, similar to a child doing homework ⛹♀️ or solving puzzles 🧠 . The AI tries out different responses 🎰 , learning from the reward model which ones are good (get high scores 🎯 ) and which ones aren't 🚫 . It's a bit like a child learning from their successes and mistakes, constantly improving 🧗♀️ .
▶ Human-in-the-loop (HITL): The learning process for AI 👾 , like for a child 🚼 , is never done 🔁 . Human trainers stay involved, continually guiding the AI. This ensures that the AI's responses are not only correct ✅ but also appropriate ☑ and sensitive ⁉ to complex human standards. It's like a parent or teacher providing ongoing guidance and feedback as a child grows into an adult 🕺 💃 .
So, RLHF in AI 🤖 is very much like raising or educating a child 🤓 . It starts with basic learning, moves to guided practice and feedback, involves learning from both success and failure and requires ongoing mentorship. This method helps AI or LLMs not just to 'know' things 🖥, but to understand and align with human values and context, much like how we educate children to become thoughtful, informed adults 👩🎓.
