Transition Thinking: Training-Free Prediction Arbitration for Reasoning Models

Training-free methods such as Soft Thinking seek latent reasoning in a frozen model by feeding probability-weighted mixtures of token embeddings back into the Transformer. However, their advantage over chain-of-thought is inconsistent across models and benchmarks. We find that the soft mixture usually stays near its dominant token embedding and rarely changes the next generated tokens, so Soft Thinking behaves much like argmax decoding. This helps explain why its gains vary. Argmax trajectories can be as accurate as sampled ones when they finish but are vulnerable to repetition loops. Soft Thinking terminates looping generations with Cold Stop, yet these generations are often incorrect. Layerwise next-token predictions reveal a transition at a specific intermediate layer, where entropy falls sharply while predictions still differ from the final layer. At this layer, hesitation and repetition loops produce opposite confidence patterns in the two layers. When the final layer hesitates, the transition layer can hold a confident alternative, whereas inside a loop the final layer is nearly deterministic and the transition layer is diffuse. We propose *Transition Thinking*, which keeps argmax decoding for thinking, arbitrates hesitations with the transition-layer prediction, and controls repetition with a thinking-time penalty and an in-place escape from verified loops. The transition layer is selected once per model from an unlabeled profile, and answer generation is unchanged. Across three Qwen3 models, Transition Thinking improves average accuracy over chain-of-thought on mathematics and science by 0.5 to 2.3 points, stays close to it on coding tasks, and shortens correct generations. It requires no training, additional forward passes, or task-specific layer search.