CLP: Collocation-Length Prediction for Zero-Loss Adaptive Multi-Token Inference

arXiv:2606.10935v1 Announce Type: new Abstract: Large language model inference is bottlenecked by autoregressive decoding, where each token requires a full forward pass. Multi-token prediction (MTP) offers a promising acceleration path, but existing approaches suffer from a fundamental architectural flaw: the MTP head for the first token competes with the backbone's own language model (LM) head, leading to severe quality degradation when predictions are accepted. We identify this head-backbone competition as the root cause of repetitive and incoherent outputs in prior MTP-based acceleration me

Source: arXiv cs.LG — read the full report at the original publisher.

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