From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers

Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit CoT expresses this computation as a sequence of autoregressively generated tokens. Latent reasoning replaces these tokens with compact continuous states, but most autoregressive latent-reasoning methods retain a left-to-right dependency among latent vectors. We introduce LLoCoT: a looped latent-reasoning framework that replaces left-to-right latent generation with iterative refinement of a compact latent workspace. A shared transformer is reapplied for a small number of refinement iterations, jointly updating the latent slots based on the prompt and the evolving workspace state. Using the refined state, a probabilistic head predicts a distribution from which latent tokens are sampled in parallel and used to condition an autoregressive decoder for answer generation. Training uses continuous representations derived from explicit CoT together with a final-answer prediction loss and likelihood-based supervision of the latent states. Across HumanEval, HumanEval+, MBPP, and MBPP+, the method achieves the highest four-benchmark mean among the evaluated methods, performing on par with Reasoning SFT while outperforming the base model, answer-only SFT, and NF-CoT. Furthermore, compact latent states and parallel slot sampling reduce the serial thought-generation bottleneck of explicit CoT and other autoregressive methods while preserving probabilistic latent modeling.