Proxy Compression for Language Modeling
Proxy compression enhances language model efficiency, significantly outperforming byte-level baselines.
Key Findings
Methodology
This study introduces a novel training scheme called proxy compression. By jointly training on raw byte sequences and compressed views generated by external compressors, the model learns to internally align compressed sequences and raw bytes. This alignment enables effective transfer between the two formats, even when training predominantly on compressed inputs.
Key Results
- In code language modeling experiments, proxy compression significantly improves training efficiency under fixed compute budgets, with gains becoming more pronounced as model scale increases.
- Proxy-trained models eventually match or surpass tokenizer approaches while operating solely on raw bytes.
- On the MBPP-Plus task, proxy compression outperforms byte-level and tokenizer baselines at the 14B model scale.
Significance
The significance of this study lies in providing a method to enhance training efficiency without sacrificing the robustness of byte-level modeling. Proxy compression allows for the efficiency benefits of compressed data during training while maintaining a simple byte-level interface at inference. This approach addresses the coupling issue present in traditional tokenizer methods and demonstrates stronger performance in large-scale models.
Technical Contribution
Technical contributions include the introduction of a new mixed-representation training scheme that enhances training efficiency without modifying model architectures. Proxy compression demonstrates strong cross-representation transfer, enabling models trained predominantly on proxy-compressed inputs to perform well on raw-byte inference.
Novelty
Proxy compression is the first to introduce a mixed-representation scheme in language model training, allowing efficient training without relying on a fixed compressor. This approach fundamentally differs from traditional tokenizer methods, offering a new perspective on handling the transition between compressed and raw bytes.
Limitations
- Proxy compression may underperform in small-scale models, particularly when cross-representation transfer is weak.
- Gzip as a proxy compressor performs poorly compared to tokenizer and neural proxies.
Future Work
Future research directions include exploring other types of proxy compressors, optimizing cross-representation transfer, and validating the method's effectiveness across more languages and tasks.
AI Executive Summary
Modern language models often rely on fixed tokenizers for training, which offers efficiency but also couples the model to the compressor. This paper introduces a novel method called proxy compression, which addresses this issue by using mixed-representation inputs during training.
Proxy compression uses compressed views generated by external compressors and raw byte sequences during training, allowing the model to learn alignment between these representations. This method not only improves training efficiency but also retains the robustness of byte-level modeling. Experiments show that proxy compression significantly outperforms byte-level baselines in code language modeling tasks and excels in large-scale models.
While proxy compression may underperform in small-scale models, its outstanding performance in large-scale models and potential for broad application make it a significant innovation in language model training. Future research can further optimize proxy compressors and explore their application in more tasks.
Deep Analysis
Background
Language model training often relies on tokenizers, which map raw input into discrete token sequences to enhance training efficiency. However, this design tightly couples the model to the compressor, leading to known issues such as prompt boundary problems and retokenization drift.
Core Problem
The core problem is how to maintain training efficiency without relying on a fixed compressor, while achieving an end-to-end byte-level interface. This is crucial for enhancing model robustness and adaptability.
Innovation
The core innovation of proxy compression is the introduction of a mixed-representation training scheme, allowing the model to handle both compressed views and raw bytes during training. This method not only improves training efficiency but also retains the robustness of byte-level modeling.
Methodology
- �� Use external compressors to generate compressed views
- �� Train with mixed compressed and raw byte inputs
- �� Model learns alignment between the two representations
- �� Inference uses only raw byte inputs
Experiments
Experiments were conducted on code language modeling tasks using the RefineCode dataset. The performance of byte-level, tokenizer, and proxy compression methods was compared, focusing on cross-representation transfer capabilities at different model scales.
Results
Results show that proxy compression significantly outperforms byte-level baselines under fixed compute budgets, especially in large-scale models. Proxy-trained models excel on the MBPP-Plus task, surpassing byte-level and tokenizer baselines.
Applications
Proxy compression can be directly applied to language model tasks requiring efficient training and robust inference, such as code generation and natural language processing.
Limitations & Outlook
Proxy compression may underperform in small-scale models, and Gzip as a proxy compressor performs poorly. Future research can optimize proxy compressors and explore applications in more tasks.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional language models are like needing to chop ingredients into specific shapes before cooking, which is convenient but sometimes affects the original flavor. Proxy compression is like putting ingredients directly into the pot while using special tools to automatically arrange them neatly. This retains the original flavor while improving cooking efficiency.
ELI14 Explained like you're 14
Imagine you're playing a game where you usually need to equip your character before going on an adventure, which is a hassle. Proxy compression is like a super gear that lets you go on adventures directly while automatically equipping everything you need! This makes the game simpler and more fun. You no longer worry about gear issues, just focus on the adventure itself.
Glossary
Proxy Compression
A training scheme that uses mixed-representation inputs to enhance language model training efficiency.
Used in training to learn alignment between compressed views and raw bytes.
Tokenizer
A tool that segments text into discrete tokens to enhance training efficiency.
Commonly used compressor in traditional language model training.
Byte-level Modeling
A method of modeling directly on raw bytes, offering robustness.
Interface used in inference by proxy compression.
Cross-representation Transfer
The ability of a model to learn and apply knowledge across different representations.
A core advantage of proxy compression.
MBPP-Plus
A benchmark for evaluating code generation models.
Task used in experiments to evaluate proxy compression performance.
Open Questions Unanswered questions from this research
- 1 How to improve proxy compression performance in small-scale models?
- 2 Are there other types of proxy compressors that can further enhance efficiency?
Applications
Immediate Applications
Code Generation
Proxy compression can be used to enhance training efficiency and inference robustness in code generation tasks.
Long-term Vision
Multilingual Processing
Proxy compression can be extended to handle multilingual tasks, improving modeling capabilities for low-resource languages.
Abstract
Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over UTF-8 byte sequences, thereby coupling the model to that compressor. This work introduces proxy compression, an alternative training scheme that preserves the efficiency benefits of compressed inputs while providing an end-to-end, raw-byte interface at inference time. During training, a single language model is jointly trained on raw byte sequences and compressed views generated by external compressors; through the process, the model learns to internally align compressed sequences and raw bytes. This alignment enables strong transfer between the two formats, even when training predominantly on compressed inputs that are discarded at inference. Extensive experiments on code language modeling demonstrate that proxy compression substantially improves training efficiency and significantly outperforms pure byte-level baselines given fixed compute budgets. As model scale increases, these gains become more pronounced, and proxy-trained models eventually match or surpass tokenizer approaches, all while operating solely on raw bytes and retaining the inherent robustness of byte-level modeling. Our code is available at https://github.com/LZhengisme/proxy-compression.