A Reddit user asks how Multi-Token Prediction (MTP) verifies generated tokens efficiently without incurring the latency of autoregressive generation. The poster is confused about how the model's last layer can confirm the correctness of N predicted tokens using only the information available at that stage.
- The user describes a scenario where MTP heads predict "green" instead of the correct "blue" and wonders how this error is detected quickly.
- They note that standard prefill populates caches, but are unsure how verification occurs without running each token through the full model depth.
- A cited explanation suggests using a causal attention mask to prefill one token at a time and verify output embeddings for each of the N tokens.
- The user challenges this by asking how decoder-only LLMs can judge work done by MTP heads if they lack sufficient context from earlier layers.
The post highlights ongoing confusion regarding the technical implementation of MTP verification in decoder-only architectures.