Recent generations of frontier language models have introduced Large Reasoning Models
(LRMs) that generate detailed thinking processes before providing answers. While these models
demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scal-
ing properties, and limitations remain insufficiently understood. Current evaluations primarily fo-
cus on established mathematical and coding benchmarks, emphasizing final answer accuracy. How-
ever, this evaluation paradigm often suffers from data contamination and does not provide insights
into the reasoning traces’…
Source: Read MoreÂ
