Trading Depth for Time in Recurrent Transformers

Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of the next. This raises a natural question: is additional computation better spent on more temporal steps or greater physical depth? We investigate this question using Latent Recurrent Transformers (LRT), which retain one backbone forward pass per vocabulary token during decoding and provide a controlled setting for comparing these two ways of adding computation. We insert a latent thought token between consecutive vocabulary tokens, adding a hidden-state refinement step through the shared backbone before predicting the next token. On 16- and 20-layer NanoChat MoE backbones, we compare this model with double-depth vanilla Transformers and LRT, two-loop Transformers, and PonderLM-2 with one thought token. All configurations execute 2L blocks per vocabulary token during decoding. Under matched training FLOPs, LRT with one thought token outperforms PonderLM-2 and overtakes the two-loop Transformer as compute increases. Its gap to vanilla double-depth narrows from 0.014 to 0.001 bits per byte, despite approximately 46% fewer parameters and approximately half as many unique training tokens. At the training budget used for our main comparison, one thought token recovers an average of 89% of the gains from doubling LRT depth across the two backbone sizes, with approximately 48% fewer parameters. These results highlight temporal thinking as a parameter-efficient way to extend computation in recurrent Transformers.