Reinforcement Learning for Reasoning in Small LLMs

Author(s): Rakib.ai

Originally published on Towards AI.

Latest Hugging Face Research On The Subject

In the race toward increasingly powerful artificial intelligence, there’s been an unspoken assumption: bigger is better.

Language models like GPT-4 and Claude boast hundreds of billions of parameters, requiring computational resources that only the wealthiest tech companies can afford.

But, behold!

A groundbreaking new paper from researchers Quy-Anh Dang and Chris Ngo presents compelling evidence that small language models can punch well above their weight class when it comes to mathematical reasoning.

Their research, titled “: What Works and What Doesn’t,” might just rewrite our assumptions about what’s possible with limited computational resources.

Most of us have marveled at AI systems solving complex problems that seem to require human-like reasoning. These advanced capabilities typically come from massive models developed by organizations with vast computational resources.

OpenAI’s o1 series, for instance, leverages extended Chain-of-Thought reasoning to excel at mathematics and scientific reasoning, but the computational costs are staggering.

“These models — often exceeding hundreds of billions of parameters — render them impractical for self-hosting by most organizations outside major technology firms,” the researchers note, highlighting a growing divide between AI haves and have-nots.

Comparison of zero-shot pass@1 performance versus model size (left) and computational cost (right). Our Open-RS (red point) achieves the highest AIME24 score (46.7%), outperforming o1-preview (44.6%)… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI