Reinforcement pre-training - baking the cherry into the cake

I love the analogy in this new Reinforcement Pre-Training (RPT) paper from Microsoft Research. They say we usually treat reinforcement learning (RL) as the “cherry on top” of a pre-trained language model, something we add at the very end for alignment. But their work asks a powerful question: what if we bake the cherry right into the cake from the start?

That’s the core idea behind their new method, Reinforcement Pre-Training (RPT). It completely reimagines the pre-training process. Instead of just having the model mindlessly predict the next word from a text corpus, RPT forces the model to first reason about what the next word should be, essentially generating a mini chain-of-thought for every single token prediction.

The Motivation Behind the Reward System

I like the motivation of the reward system. It’s incredibly simple and scalable:

1. The model “thinks” about the next token
2. It makes its prediction
3. If it’s correct, it gets a reward. If not, it doesn’t

This simple loop turns the vast, unannotated text on the internet into a massive, free dataset for reinforcement learning, no human labeling required.

Why This Matters

It builds fundamentally stronger models. By encouraging reasoning over rote memorization from day one, the base models become far more capable.

It shows great scaling laws. The more compute you throw at it, the better the model’s reasoning gets, which is exactly what you want to see for a new paradigm.

The zero-shot performance is pretty solid. Their RPT-trained 14B model actually beats a standard 32B model on tough reasoning benchmarks like MMLU-Pro.

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This feels less like an incremental improvement and more like a foundational shift in how we could build the next generation of models, making them powerful reasoners right from their initial training. Really smart stuff.

We grow sometimes in one dimension, and not in another, unevenly. We grow partially. We are relative. We are mature in one realm, childish in another. —But perhaps our models can grow more holistically when we teach them to reason from the very beginning

Paper: Microsoft Research RPT Study