LatentMT: Machine Translation with Latent Reasoning
LatentMT introduces latent-reasoning loops into a 2.6B-parameter machine translation model, enabling it to match the performance of models three to five times larger across 32 translation directions. The model achieves state-of-the-art results on mid- and low-resource languages and demonstrates that recurrent computation within hidden states can improve translation quality efficiently. LatentMT also requires less training and inference compute compared to larger models.
Why it matters: This work suggests a new, more efficient scaling path for machine translation by leveraging latent recurrent computation, potentially reducing resource requirements for high-quality translation.
Full story at: arXiv Computation and Language ↗