Hierarchical Latent Prediction for Language Models

Abstract:While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optimal for long-horizon reasoning and planning. Recent works such as Multi-Token Prediction (MTP) and Next-Latent prediction (NextLat) try to mitigate the problem through predicting multiple future tokens and self-supervised prediction in the latent space. However, those auxiliary objectives either have a limited horizon or suffer from compounding error from multi-step rollout. We introduce Hierarchical Latent Prediction (HiLP), which introduces an auxiliary higher-level abstract latent to help reduce the error accumulation effect in latent-space rollouts. Experiments show that HiLP can lead to longer-horizon coherent belief state representation and demonstrate the effectiveness of our method across coding and multi-step reasoning benchmarks, and offers more speculative decoding efficiency.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.05806 [cs.CL]

(or arXiv:2608.05806v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05806 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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