How Reinforcement Learning Can Improve Language Models
Large Language Models (LLMs) are great, but they’re not perfect. Sometimes, they make mistakes when solving math problems or writing code. Wouldn’t it be nice if these models could recognize their own errors and fix them without any human help?
This is something called self-correction, and a new research paper by Google DeepMind presents a technique called SCoRe, which stands for Self-Correction via Reinforcement Learning.
Why Self-Correction is Important
We can use LLMs in many areas, but sometimes they get stuck. They have the knowledge and data, but they don’t always apply it correctly. For example, they might start solving a math proof correctly but get lost halfway through, however they just keep going with it which often the outcome and result they ended up with, is incorrect. If these models could stop and say, “Oops, I made a mistake, let me fix that,” they could become much more reliable, but most of the LLMs are not train for that and that’s where SCoRe comes in.
What is SCoRe?
SCore is a new method that teaches models how to fix their own errors using Reinforcement Learning (RL). In simple words, the model learns from it’s own attemps to solve a problem. Instead of giving the model the correct solution (Like…
