"Decoding Looped Transformers Better for (Almost) Free" This paper treats an early loop from a looped transformer as a rough draft and the final loop as the refined answer. It then uses the difference between them to push the final prediction further in the direction the model was already improving, with no extra training. On Ouro-2.6B-Thinking, AIME 2024 accuracy jumps from 61.9% to 73.3% And using only half the recurrent loops can still match or beat full-depth decoding while cutting…