Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning

Summary

This paper talks about a study on how AI models can reason across different kinds of problems or domains, like math, coding, and puzzles, using a method called Reinforcement Learning with Verifiable Rewards (RLVR).

What's the problem?

Most existing AI training focuses on one type of problem at a time, but real-world situations often require solving many different kinds of problems together, and it's not well understood how training on multiple domains affects overall reasoning.

What's the solution?

The researchers used a special algorithm and a family of AI models to systematically study how training on one domain helps or hurts performance on others. They also looked at how combining domains, using guided learning strategies, and changing reward designs impacts the AI’s ability to reason both within and across domains.

Why it matters?

This matters because understanding how AI can learn to think across multiple domains helps build smarter and more flexible AI systems that can solve a wide range of problems more effectively, much like humans do.