Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model

• URL: http://arxiv.org/abs/2605.30080v1
• Date: Thu, 28 May 2026 15:26:34 GMT
• Title: Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model
• Abstract summary: Tokenization-free hierarchical models are emerging as a promising alternative to traditional Large Language Models.<n>We propose Adaptive Targeted Dynamic Chunking (ATDC), a novel byte-compression control mechanism.
• Score: 1.283285810929198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tokenization-free hierarchical models are emerging as a promising alternative to traditional Large Language Models (LLMs), addressing inherent preprocessing issues such as vocabulary design complexity, out-of-vocabulary (OOV) errors, and language-specific constraints. However, a significant challenge in these byte-level methods is the optimization of the compression ratio, a critical factor that dictates model performance for processing bytes data via chunks. In this paper, we propose Adaptive Targeted Dynamic Chunking (ATDC), a novel byte-compression control mechanism designed to enhance the effectiveness of dynamic chunking within hierarchical architectures. Our approach utilizes curriculum learning to progressively adjust the compression ratio during training, transitioning from low to high compression to stabilize the learning process. We provide an analysis establishing the relationship between the target compression ratio and Bytes-Per-Innermost-Chunk (BPIC), allowing for tracking of chunk-size evolution throughout the training phase. Evaluations conducted on the FineWeb-Edu 100B dataset demonstrate that hierarchical models equipped with ATDC achieve competitive Bits-Per-Byte (BPB) performance compared to conventional baselines operating at both byte and token levels. Furthermore, the proposed method exhibits more stable training dynamics and superior final performance across diverse downstream tasks compared to models using fixed compression ratios, while maintaining the inherent robustness and flexibility of byte-level processing.

Related papers

• Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models [102.20309135516186]
  Cross-entropy (CE) training provides dense and scalable supervision for language models.<n>We introduce a feature-matching objective for language-model fine-tuning.<n>To optimize this objective efficiently, we propose energy-based fine-tuning.
  arXiv  Detail & Related papers  (2026-03-12T17:57:50Z)
• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• Proxy Compression for Language Modeling [58.904023114033954]
  proxy compression is an alternative training scheme that preserves the efficiency benefits of compressed inputs.<n>Experiments on code language modeling demonstrate that proxy compression substantially improves training efficiency.<n>As model scale increases, proxy-trained models eventually match or rival tokenizer approaches.
  arXiv  Detail & Related papers  (2026-02-04T07:36:46Z)
• Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation [75.58269386927076]
  Autoregressive (AR) models are often dismissed as impractical due to prohibitive computational cost.<n>This work re-thinks this paradigm, introducing a framework built on hierarchical parallelism and progressive adaptation.<n> Experiments on diverse datasets (natural, satellite, medical) validate that our method achieves new state-of-the-art compression.
  arXiv  Detail & Related papers  (2025-11-14T06:27:58Z)
• CCF: A Context Compression Framework for Efficient Long-Sequence Language Modeling [52.05149789178508]
  CCF is a novel context compression framework designed to enable efficient long-context modeling.<n>CCF integrates segment-wise semantic aggregation with key-value memory encoding, forming compact representations.<n> Empirical results on multiple long-context language modeling benchmarks demonstrate that CCF achieves competitive perplexity under high compression ratios.
  arXiv  Detail & Related papers  (2025-09-11T07:13:49Z)
• FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression [15.784158079414235]
  FLAT-LLM is a training-free structural compression method based on fine-grained low-rank transformations in the activation space.<n>It achieves efficient and effective weight compression without recovery fine-tuning, which could complete the calibration within a few minutes.
  arXiv  Detail & Related papers  (2025-05-29T19:42:35Z)
• Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning [0.0]
  Contextual Compression.<n>(CCE) introduced a multi-stage encoding mechanism that dynamically restructured parameter distributions.<n>CCE retained linguistic expressivity and coherence, maintaining accuracy across a range of text generation and classification tasks.
  arXiv  Detail & Related papers  (2025-02-12T11:44:19Z)
• Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation [10.376875638696504]
  This work presents Any Compression via Iterative Pruning (ACIP), a novel algorithmic approach to determine a compression-performance trade-off.<n>We use an SVD-reparametrization of linear layers and iteratively prune their singular values with a sparsity-inducing penalty.<n>We show that ACIP seamlessly complements common quantization-based compression techniques.
  arXiv  Detail & Related papers  (2025-02-03T18:40:58Z)
• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding [55.107555305760954]
  We propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. Our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.
  arXiv  Detail & Related papers  (2024-01-17T09:01:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.