LoopMTP: A looped transformer guided by latent multi-token prediction

Researchers propose LoopMTP, a looped transformer that uses latent multi-token prediction to guide intermediate representations, improving average accuracy by up to 8.1% relative over non-looped baselines while maintaining stable training for up to 15 loops. The method softly aligns hidden states with future token embeddings and uses a gating mechanism to preserve information across iterations.

arXiv:2608.03624v1 Announce Type: new Abstract: Looped transformers have emerged as a parameter-efficient alternative to scaling depth for strong reasoning. By reusing one stack of layers across $T$ iterations, they attain the effective depth and reasoning capabilities of larger models at a fixed parameter count. Yet existing approaches suffer from latent overthinking and undifferentiated computation, largely because intermediate representations receive no guidance across loops. Multi-token prediction MTP supplies exactly the dense, forward-looking supervision the loop is missing. We propose \textsc{LoopMTP}, which links the two through a structural correspondence in latent space: a model that loops $T$ times can anticipate $T$ future tokens. \textsc{LoopMTP} realizes this by softly aligning the hidden state of loop $t$ with the embedding of the token $t$ steps ahead, while a lightweight gate preserves useful information across iterations. \textsc{LoopMTP} improves average accuracy by up to 8.1\% relative over the non-looped baseline, with training remaining stable for up to 15 loops.