Machine Learning - Let’s Verify Step by Step

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're tackling a paper that's all about making AI smarter, specifically when it comes to complex problem-solving – think of it like teaching a robot to not just memorize answers, but to actually understand how to get there.

So, we all know those AI models, the large language models, that are getting pretty good at doing complex things. They can write stories, answer questions, even try to solve math problems. But here's the thing: even the best ones still make silly mistakes, like getting basic logic wrong. It's like that friend who's generally brilliant but occasionally puts their shoes on the wrong feet!

Now, how do we fix this? Well, the researchers behind this paper looked at two main ways to train these models:

• Outcome Supervision: This is like giving a student a grade only on their final exam. You tell them if the answer is right or wrong, but you don't give them feedback on how they got there.
• Process Supervision: This is like a teacher going through each step of a student's work, pointing out where they went wrong and why. You give feedback on each intermediate step, not just the final answer.

Think of it like learning to bake a cake. Outcome supervision is like tasting the finished cake and saying "too sweet!" Process supervision is like someone watching you add ingredients, saying, "Whoa, hold on! That's way too much sugar for this recipe!"

The researchers wanted to figure out which method works best, especially since getting feedback from humans (that process supervision part) can be really expensive and time-consuming. Previous studies have scratched the surface, but this paper goes deeper.

And guess what? They found that process supervision wins, big time! They trained models to solve problems from a really tough math dataset called MATH. The model trained with process supervision aced a whopping 78% of the problems in a test set. That's a huge jump!

"Process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset."

But it doesn't stop there! They also looked at something called active learning. This is like letting the AI model choose which problems it wants to be trained on. The model basically says, "Hey, I'm really struggling with this type of problem, can you give me some extra feedback on that?" Turns out, active learning makes process supervision even more effective!

To help other researchers, they're releasing a massive dataset of human feedback labels – 800,000 of them! It's called PRM800K, and it's a treasure trove for anyone working on improving AI reasoning.

So, why does all this matter? Well, better AI reasoning has implications for everything from medical diagnosis to financial modeling. Imagine AI that can reliably solve complex problems in healthcare, leading to more accurate diagnoses and personalized treatments. Or AI that can make smarter financial decisions, helping people manage their money more effectively.

Here are a few things I was pondering as I read this:

• If process supervision is so much better, why aren't we using it all the time? Is the cost of human feedback truly the only barrier?
• Could we develop AI tools to automatically provide process supervision, reducing the need for expensive human input?
• Beyond math, what other domains could benefit most from this type of process-supervised AI training?

This research is a big step forward in building more reliable and trustworthy AI. It's exciting to think about the possibilities! What do you guys think? Let me know your thoughts in the comments!

Credit to Paper authors: Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe