Base models already contain reasoning capabilities encoded in their training data—you can unlock them by conditioning on the right token cues, without needing expensive RL fine-tuning.
This paper shows that base language models can achieve reasoning performance comparable to RL-trained models by using specific starting tokens (like "Okay" or "Alright") that trigger learned associations from training data. The authors demonstrate they can create new reasoning cues through data interventions and trace these effects back to specific document types in the training set.