MUX: Continuous Reasoning via Multiplexed Tokens
A new method called MUX enables language models to perform more efficient reasoning by distilling discrete reasoning steps into continuous multiplexed tokens in a latent space. This approach uses lossless superposition of subword spans, allowing for parallel exploration in search problems and more compact reasoning. In evaluations across 32 settings and four language models, MUX outperformed strong latent reasoning baselines. Analyses indicate that the latent tokens encode faithful and interpretable reasoning.
Why it matters: MUX demonstrates a novel and practical advance in language model reasoning efficiency, potentially reducing computational bottlenecks by enabling higher-bandwidth latent representations.
Full story at: arXiv AI/ML ↗