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arXiv · 2608.05806

Hierarchical Latent Prediction for Language Models

Chang Shi·Tim Pearce·Manan Tomar·Siddhartha Sen·John Langford
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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.

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Chang Shi, Tim Pearce, Manan Tomar, Siddhartha Sen, John Langford. 2026-08-06. Hierarchical Latent Prediction for Language Models. https://arxiv.org/abs/2608.05806

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