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Multi-Token Prediction

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Multi-token Residual Prediction
Multi-Token Residual Prediction (MRP) adapts multi-token prediction for diffusion language models by training a small module to predict the residual between adjacent denoising steps rather than the full next-step distribution. This residual target is easier to learn, allowing a tiny 3-layer transformer to accurately predict corrections across multiple steps. In the static regime, MRP enables lossless speculative decoding with up to 1.56× throughput in SGLang, or direct decoding with a small quality cost. In the dynamic regime, MRP recovers up to +16 accuracy points lost by aggressive low-threshold decoding through a remasking mechanism. The module is trained on a frozen backbone and composes cleanly with existing inference methods.

The best engineering reads, once a week.

Topic

1 post about Multi-Token Prediction. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Modal
Multi-token Residual Prediction
Multi-Token Residual Prediction (MRP) adapts multi-token prediction for diffusion language models by training a small module to predict the residual between adjacent denoising steps rather than the full next-step distribution. This residual target is easier to learn, allowing a tiny 3-layer transformer to accurately predict corrections across multiple steps. In the static regime, MRP enables lossless speculative decoding with up to 1.56× throughput in SGLang, or direct decoding with a small quality cost. In the dynamic regime, MRP recovers up to +16 accuracy points lost by aggressive low-threshold decoding through a remasking mechanism. The module is trained on a frozen backbone and composes cleanly with existing inference methods.

The best engineering reads, once a week.