Extended Data Fig. 1: Evolution of reasoning-related linguistic features in model outputs across training steps.
From: DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
a, Frequency of representative reflective terms in model-generated outputs throughout the training process. Reflective terms—including ‘wait’, ‘mistake’, ‘however’, ‘but’, ‘retry’, ‘error’, ‘verify’, ‘wrong’, ‘evaluate’ and ‘check’—were identified and curated by a panel of three human experts. Each expert independently proposed a set of words indicative of reflective reasoning, which were subsequently consolidated through consensus into a final vocabulary list. b, Frequency of the term ‘wait’ in model outputs over the course of training. This term was virtually absent during the initial training stages, appeared sporadically between steps 4,000 and 7,000 and exhibited a marked increase in frequency after step 8,000. These trends suggest the emergence of temporal reasoning or self-monitoring behaviour as training progresses.
