Large Language Models (LLMs) are incredible, but even they make mistakes. Imagine if these models could self-correct their errors without any human intervention. That’s the idea behind a new technique called SCoRe – Self-Correction via Reinforcement Learning introduced in a research paper by Google DeepMind.
Large Language Models (LLMs) are amazing, but they aren’t flawless. They sometimes make errors in math problems or coding. Wouldn’t it be incredible if these models could recognize and correct their mistakes autonomously? Enter SCoRe – Self-Correction via Reinforcement Learning, a novel technique developed by Google DeepMind.
LLMs have vast potential across various domains, but they can hit roadblocks. Despite having access to vast knowledge and data, they may misapply it, leading to incorrect outcomes. This is where SCoRe steps in, teaching models to rectify their own errors using Reinforcement Learning (RL).
SCoRe enables models to learn from their attempts and enhance their problem-solving skills. Rather than being spoon-fed correct solutions, models can now self-reflect and self-improve, paving the way for more reliable and accurate outcomes.