Can a simple training tweak make language models think better?
Researchers found a way to use scaled reinforcement training to push models toward better step-by-step thinking, without extra labeled examples.
But not all models start the same — some already show an “aha” reasoning spark from pretraining, while others learn more only after the new training.
The team also saw a hidden problem: a training habit that makes models write longer replies, even when they are wrong.
That bloats answers and wastes tokens, so they fixed it with a cleaner optimizer that keeps answers lean and still smart.
Using those ideas, a small 7B model reached a new peak of 43.
3% on a tough math test, with a stripped-down recipe anyone can try.
This work points to useful ways to make language systems clearer, faster and less noisy, but it also warns that some gains come from model quirks, not just the training trick.
Try this, watch the model, and you might see reasoning improve in ways you didn't expect.
Read article comprehensive review in Paperium.net:
Understanding R1-Zero-Like Training: A Critical Perspective
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