Just yesterday, I wrote about #Google’s co-scientist system. #Meta on 20th Feb has introduced MLGym—which is an experimental framework designed to train AI research agents to perform real-world scientific tasks.

Are they both related? Yes, at a high level both focus on using AI to assist researchers, but they take different approaches.

👉 Google’s co-scientist is like a lab assistant, helping scientists with experiments, analyzing results, and even writing research papers.

👉 Meta’s MLGym is like a training ground for AI models, teaching them how to become better research agents through real-world AI tasks.

🌟 Let us try to understand MLGym –

Meta’s MLGym is like a Gym for AI models—a space where different AI systems can train, experiment, and improve their scientific reasoning skills. Think of it like a science fair for AI agents. Just like students test different ideas and experiments, AI agents in MLGym try to generate hypotheses, analyze data, and optimize models.

MLGym includes:
◾ MLGym-Bench – A set of 13 AI research challenges across fields like computer vision, NLP, and game theory.
◾ A Modular Framework – New tasks and datasets can be easily added to improve AI learning.
◾ An Agentic Harness – AI models can be tested under real-world constraints, simulating how they’d perform in actual research.

What makes this significant is that is provides us with a great opportunity to focus on scientific breakthroughs. Today’s AI models can improve existing algorithms, but are limited in areas like:
💠 Coming up with completely new ideas.
💠 Designing novel experiments from scratch
💠 Thinking beyond existing patterns.

I believe that Meta’s framework can push AI towards deeper scientific reasoning—helping AI learn like a scientist, not just predict like a chatbot.

👉 What’s Next?

With Google, Meta, and others building AI research assistants, we are slowly inching toward the day when AI can independently drive scientific breakthroughs.

And who knows, in the future there could be a real possibility that models can be trained in Meta’s MLGym and then join systems like Google’s co-scientist multiagent ecosystem. Theoretically, this is possible. But from a technical standpoint, a lot needs to be in place first.

Both systems need common interfaces, standard data formats, and a modular design to work together. In short, we need the right bridges to connect these frameworks, and that depends on collaboration and industry standards (something that I strongly believe has to be drafted)

We’re still in the early stages, but if these technical pieces align, the future could allow AI models to be trained in one system and deployed in another, making scientific discovery more efficient.

Read the paper: https://arxiv.org/pdf/2502.14499