Two fixed opening tokens lift base model math accuracy to RL level, MIT study finds
A paper by researchers at MIT, UC Berkeley, the University of Washington and the Allen Institute for AI (Ai2) reports that pre-fixing just the first two tokens of a base model's answer can sharply raise math reasoning accuracy. On MATH-500, Olmo-3-7B rose from roughly 42% to about 78% when forced to start with “.\n\nOkay”, beating its RL-trained counterpart at about 75%, while Qwen3-14B improved from 72% to 87% with “ Alright,” — all without changing any model parameters. The authors also found the largest pre/post-RL prediction shift sits in the first two tokens, and that retraining a model with “Chicken” swapped in for “Okay” in reasoning-adjacent data made “Chicken” work as a reasoning cue too.
It suggests some reasoning ability in base models is latent and can be surfaced with a cheap prompt-side trick rather than retraining, though the finding has only been shown on two models.