Hierarchical Latent Prediction for Language Models
HiLP method introduces higher-level abstract latents to enhance long-horizon reasoning in language models.
Key Findings
Methodology
HiLP introduces higher-level abstract latents using sliding-window attention and multi-scale self-predictive learning to enhance long-horizon reasoning in language models. Specifically: 1. Sliding-window attention generates higher-level latents; 2. Higher-level dynamics model predicts k steps ahead; 3. Combined NTP head leverages abstract lookahead for improved predictions.
Key Results
- On the HumanEval benchmark, HiLP improves code generation accuracy to 11.33%, significantly better than NextLat's 10.58%.
- In speculative decoding on Nemotron-ClimbMix data, HiLP achieves an average match rate of 0.938 at K=1, outperforming NextLat's 0.918.
- HiLP shows lower future prediction error in long-horizon latent rollout, indicating more stable multi-step rollout.
Significance
HiLP is significant in academia and industry, especially for tasks requiring long-horizon reasoning and planning. By introducing hierarchical structure, HiLP effectively addresses the error accumulation problem in traditional NTP methods, offering a new perspective for language model pretraining.
Technical Contribution
HiLP's technical contributions include introducing multi-scale self-predictive learning and sliding-window attention to address shortcomings in existing methods for long-horizon reasoning. By hierarchical prediction in latent space, HiLP achieves more stable multi-step rollout.
Novelty
HiLP is the first to introduce hierarchical latent prediction in language model pretraining, enhancing long-horizon prediction without increasing inference latency, distinguishing it from existing methods.
Limitations
- HiLP still relies on the standard NTP head during inference, which may limit performance in complex tasks.
- The prediction horizon for higher-level latents is manually set, lacking dynamic adjustment capability.
Future Work
Future work could explore dynamically adjusting the prediction horizon for higher-level latents and the possibility of using both NTP and combined NTP heads during inference.
AI Executive Summary
In the field of natural language processing, existing language models struggle with long-horizon reasoning and planning tasks, primarily due to a mismatch between training and inference phases. Traditional Next-Token Prediction (NTP) methods rely on their own outputs during inference, leading to error accumulation. To address this, the paper introduces a new method—Hierarchical Latent Prediction (HiLP).
HiLP enhances long-horizon reasoning by introducing higher-level abstract latents and sliding-window attention. In experiments, HiLP demonstrates superior performance across multiple benchmarks, particularly in code generation and multi-step reasoning tasks. Compared to existing methods, HiLP not only improves accuracy but also significantly enhances speculative decoding efficiency.
However, HiLP has limitations, such as the manual setting of the prediction horizon for higher-level latents. Future research could explore methods for dynamically adjusting the horizon and the possibility of using different prediction heads during inference to further enhance model performance and applicability.
Deep Analysis
Background
In recent years, language models have made significant progress in the field of natural language processing, especially those based on the Transformer architecture. However, existing models still face challenges in long-horizon reasoning and planning tasks. Traditional NTP methods rely on their own outputs during inference, leading to error accumulation and affecting generation quality. To address this, researchers have proposed various improvements, such as Multi-Token Prediction (MTP) and Next-Latent Prediction (NextLat), but these methods still have limitations in long-horizon reasoning.
Core Problem
Existing language models perform poorly in long-horizon reasoning tasks, primarily due to a mismatch between training and inference phases. NTP methods rely on their own outputs during inference, leading to error accumulation and affecting generation quality. How to enhance long-horizon reasoning without increasing inference latency is the core problem of current research.
Innovation
The core innovation of the HiLP method lies in introducing higher-level abstract latents and sliding-window attention. By hierarchical prediction in latent space, HiLP achieves more stable multi-step rollout. Unlike existing methods, HiLP enhances long-horizon prediction without increasing inference latency.
Methodology
- �� HiLP generates higher-level latents using sliding-window attention.
- �� A higher-level dynamics model predicts k steps ahead in the abstract space.
- �� Combined NTP head leverages abstract lookahead information for improved subsequent predictions.
- �� Training objectives include standard NTP loss, NextLat transition consistency loss, KL term, higher-level transition consistency loss, and combined NTP loss.
Experiments
Experiments are conducted on 1B-parameter models using 8×NVIDIA B200 GPUs, with training data including 100B tokens. Evaluation benchmarks include the HumanEval code generation benchmark and DataComp multi-step reasoning benchmark. Results show that HiLP performs excellently across multiple benchmarks, particularly in code generation and multi-step reasoning tasks.
Results
Experimental results show that HiLP improves code generation accuracy to 11.33% on the HumanEval benchmark, significantly better than NextLat's 10.58%. In speculative decoding on Nemotron-ClimbMix data, HiLP achieves an average match rate of 0.938 at K=1, outperforming NextLat's 0.918.
Applications
HiLP has broad application prospects in tasks requiring long-horizon reasoning and planning, such as code generation, automated reasoning, and complex task planning. It enhances long-horizon prediction without increasing inference latency, offering significant industrial application value.
Limitations & Outlook
HiLP still relies on the standard NTP head during inference, which may limit performance in complex tasks. Additionally, the prediction horizon for higher-level latents is manually set, lacking dynamic adjustment capability. Future research could explore methods for dynamically adjusting the horizon and the possibility of using different prediction heads during inference.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional NTP methods are like checking each step as you go, which can lead to mistakes. HiLP is like planning the entire recipe in advance, knowing what to do at each step, so even if there are small errors, the final dish isn't affected. By introducing higher-level abstract latents, HiLP forms a complete recipe in your mind, helping you better complete the cooking process.
ELI14 Explained like you're 14
Imagine you're playing a strategy game that requires long-term planning. Traditional methods are like stopping to think about the next move at each step, which can lead to mistakes. HiLP is like planning the entire game strategy in advance, knowing what to do at each step, so even if there are small errors, the final victory isn't affected. By introducing higher-level abstract latents, HiLP forms a complete game strategy in your mind, helping you better complete the game.
Glossary
Next-Token Prediction (NTP)
A language model training method that learns sequence information by predicting the next token.
NTP is one of the foundational training objectives of the HiLP method.
Latent Variable
A hidden variable used to capture underlying structures in data.
HiLP enhances long-horizon reasoning by introducing higher-level latent variables.
Sliding-Window Attention
An attention mechanism that computes attention within a fixed window to generate higher-level latents.
Used in HiLP to generate higher-level abstract latents.
Multi-step Rollout
A prediction method that captures long-term dependencies through multi-step prediction.
HiLP enhances long-term prediction stability through multi-step rollout.
Self-supervised Learning
A learning method that trains using information inherent in the data without manual labeling.
HiLP uses self-supervised learning for prediction in latent space.
Open Questions Unanswered questions from this research
- 1 How to dynamically adjust the prediction horizon for higher-level latents to suit different task requirements?
- 2 The possibility and impact of using both NTP and combined NTP heads during inference.
Applications
Immediate Applications
Code Generation
HiLP can be used to improve the accuracy of code generation tasks, especially in complex code generation requiring long-horizon reasoning.
Long-term Vision
Complex Task Planning
HiLP has broad application prospects in complex task planning, enhancing the efficiency and accuracy of task planning.
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.