arXiv:2511.11346v2 Announce Type: replace Abstract: Multi-token prediction (MTP) is a prominent strategy to significantly speed up generation in large language models (LLMs), especially in byte-level LLMs, which are tokeniser-free but prohibitively slow. However, many existing MTP methods either assume independence between future tokens, sacrificing expressiveness, or generate tokens one at a time within the window, increasing latency. In this work, we investigate the trade-off between expressiveness and latency in MTP within the framework of probabilistic circuits (PCs). Our framework, MTPC,
The increasing scale and computational demands of Large Language Models (LLMs) are driving research into more efficient generation methods, making improvements in multi-token prediction critical for practical applications.
This research directly addresses a significant bottleneck in LLM performance, potentially enabling faster and more practical byte-level LLMs for a wider range of applications, including those requiring tokeniser-free operations.
The trade-off between expressiveness and latency in multi-token prediction within LLMs is being optimized through new probabilistic circuit frameworks, offering a path to more efficient and capable models.
- · AI developers
- · Cloud computing providers
- · LLM-dependent applications
- · Data center operators
- · Less efficient LLM architectures
- · High-latency AI services
Faster LLM inference reduces computational costs and improves user experience for AI applications.
The proliferation of more efficient byte-level LLMs could democratize access to advanced AI capabilities by reducing infrastructure requirements.
Enhanced LLM efficiency might accelerate the development and deployment of sophisticated AI agents by providing a more responsive foundation.
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Read at arXiv cs.LG