arXiv cs.CLPaper
Base Models Can Reason By Taking a Cue From Training Data
This explains a chunk of why RLHF works: it's surfacing latent capabilities that already exist in the base model through token patterns learned during pretraining. For practitioners, it means you can experiment with prompt prefixes as a cheaper alternative to fine-tuning for reasoning tasks. The causal data interventions suggest that reasoning quality is more about triggering the right associations than about learning new computation.