Everyone assumes that complex reinforcement learning is the secret sauce for teaching AI to reason. But what if the secret isn’t the sophisticated reward algorithm, but simply feeding the model more diverse data in a straightforward way? This question challenges the expensive, compute-heavy trends dominating modern AI development.

Researchers investigated next-chunk reasoning reinforcement learning, a method designed to help models learn from raw text like textbooks that lack explicit step-by-step thinking traces. The idea is to reward the model for predicting the next part of a solution, effectively forcing it to generate implicit reasoning. However, critics argue that the real benefit might just be better exposure to this rich data, not the complex RL framework itself.

To test this, the team compared the RL approach against a surprisingly simple alternative: Mixed SFT. This method simply combines the raw reasoning data with standard long-chain-of-thought examples in a single supervised training step. The results were startling. Mixed SFT not only outperformed the complex RL method but did so using over sixty times less computational power. This advantage held true for both in-domain mathematical reasoning and out-of-domain reasoning tasks.

The study delivers a crucial warning for developers chasing performance gains. Higher accuracy before reinforcement learning does not guarantee better results afterward. The findings suggest that simpler, more efficient training pipelines can beat complex, resource-heavy methods. It is time to rethink whether we are optimizing for actual intelligence or just for expensive compute budgets.