Inference
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
In depth
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk. Meta's 2024 paper found it improved sample efficiency and coding benchmarks, with a 13B model solving 12% more HumanEval problems than a next-token baseline. The extra heads can also be reused at inference as a built-in draft for speculative decoding, which made 4-token models up to 3x faster.
Primary source: arxiv.org
Example
In practice, developers reach for Multi-Token Prediction (MTP) when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Multi-Token Prediction (MTP)?
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
Why does Multi-Token Prediction (MTP) matter for AI developers?
Multi-Token Prediction (MTP) sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Multi-Token Prediction (MTP)?
Developers Digest publishes tutorials and videos that cover Inference topics including Multi-Token Prediction (MTP). Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Next-Token Prediction
The training objective behind GPT-style language models: given a sequence of tokens, predict a probability distribution over the token that comes next.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
Inference
Mixture of Experts (MoE)
A model architecture that routes each input to a small subset of specialized sub-networks ("experts") rather than activating the entire model.
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