Self-Distillation for Multi-Token Prediction

📰 ArXiv cs.AI

Self-Distillation for Multi-Token Prediction (MTP-D) improves Large Language Models' inference efficiency by predicting multiple future tokens in parallel

advanced Published 26 Mar 2026

Action Steps

Identify the challenges in existing Multi-Token Prediction approaches, such as limited acceptance rates and joint training difficulties
Apply self-distillation to improve the performance of MTP heads
Implement MTP-D, a simple yet effective self-distillation method, to accelerate LLM inference
Evaluate the effectiveness of MTP-D in various sequence prediction tasks

Who Needs to Know This

AI engineers and researchers working on Large Language Models can benefit from MTP-D as it accelerates inference efficiency, while machine learning researchers can apply this method to other sequence prediction tasks

Key Insight

💡 Self-distillation can improve the performance of Multi-Token Prediction heads, leading to more efficient Large Language Model inference

Key Takeaways

Self-Distillation for Multi-Token Prediction (MTP-D) improves Large Language Models' inference efficiency by predicting multiple future tokens in parallel

Full Article

Title: Self-Distillation for Multi-Token Prediction

Abstract:
arXiv:2603.23911v1 Announce Type: cross Abstract: As Large Language Models (LLMs) scale up, inference efficiency becomes a critical bottleneck. Multi-Token Prediction (MTP) could accelerate LLM inference by predicting multiple future tokens in parallel. However, existing MTP approaches still face two challenges: limited acceptance rates of MTP heads, and difficulties in jointly training multiple MTP heads. Therefore, we propose MTP-D, a simple yet effective self-distillation method with minimal

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