Token Deduplication Techniques

Updated 14 August 2025

Token deduplication is the systematic process of identifying and removing repeated or semantically redundant tokens to enhance storage efficiency and computational speed.
It employs advanced algorithms like MinHash LSH and locality-aware strategies that achieve high deduplication ratios, with metrics such as Jaccard similarity ≥0.96 and improvements up to 45% in disk usage reduction.
Hybrid and semantic approaches, including HPDedup and token cleaning frameworks, balance effective removal of redundancy with preservation of useful information, yielding improvements in training speed and model accuracy.

Token deduplication is the systematic identification and elimination of repeated or semantically redundant tokens across a data collection or within data processing pipelines, with substantial impact on storage efficiency, computational cost, model quality, and system scalability. Modern token deduplication leverages hashing, statistical locality measures, active learning, and topological modeling to maximize deduplication ratios while minimizing adverse effects such as fragmentation, bias, or loss of useful information.

1. Foundational Algorithms and Hash-Based Deduplication

MinHash Locality Sensitive Hashing (LSH) forms the cornerstone of large-scale token deduplication for datasets used in training LLMs and other AI systems. In practice, tokens (or documents) are first broken into n-gram shingles, then transformed via custom non-cryptographic hash functions for computational efficiency (e.g., $h(s) = f(s) \bmod p$ for a shingle $s$ ) (Son et al., 2 Jan 2025). Successive, overlapping shingles allow for hash result reutilization, drastically reducing computation. Hash signatures are partitioned into bands, with candidate duplicates formed when tokens share an identical band (thresholding at Jaccard similarity $\sim$ 85% for high selectivity (Tokpanov et al., 9 Nov 2024)). Comprehensive GPU-accelerated frameworks, such as FED, utilize pairwise comparisons within buckets to minimize false negatives; deduplication results show Jaccard similarity $\geq$ 0.96 to classical algorithms.

2. Locality-Aware and Hybrid Deduplication Mechanisms

In primary storage and cloud contexts, temporal and spatial locality inference informs deduplication strategies. HPDedup integrates inline and post-processing deduplication, with inline caching guided by real-time estimation of the Local Duplicate Set Size (LDSS), computed as $LDSS_{(i)} = N_{i} - u_{i}$ —where $N_{i}$ is the number of write requests and $u_{i}$ the unseen unique fingerprints per stream (Wu et al., 2017). Streams with lower LDSS (high temporal locality) receive prioritized cache allocation (priority $p_{i}=1/LDSS_{(i)}$ ), whereas spatial locality statistics dynamically set block deduplication thresholds ( $T = (1-r)\bar{L}_{d} + r\bar{L}_{r}$ ), balancing write latency and fragmentation. HPDedup's hybrid approach yields up to 39.70% improvement in inline deduplication efficiency and 45.08% in disk usage reduction over iDedup and related schemes.

3. Semantic and Influence-Based Token Cleaning

Recent methodologies shift from sample-level filtering to token-level quality assessment. The Token Cleaning framework quantifies token informativeness using the influence of model updates (loss difference per token): $Infl(x_{(i,j)}|\vec{x}_{(i,:j)};\theta,\theta') = \ell(x_{(i,j)}|\vec{x}_{(i,:j)};\theta') - \ell(x_{(i,j)}|\vec{x}_{(i,:j)};\theta)$ , where $\theta$ and $\theta'$ are model parameters before and after fine-tuning (Pang et al., 4 Feb 2025). Tokens with top-k% influence scores are preserved, yielding improved downstream performance (average +6.3% over full set baselines for LLaMA and Mistral models). Both fixed-model and self-evolving strategies exist, the latter iteratively refines the reference model and improves selection granularity.

4. Deduplication in Vision and Diffusion Models

Token deduplication in neural architectures frequently involves pruning or merging strategies guided by attention or feature similarity. In Vision Transformers, static Top-K attention pruning and dynamic clustering (e.g., ToMe/K-Medoids/DPC-KNN) are prominent (Haurum et al., 2023). Pruned tokens are either dropped or fused (as in EViT: $\text{fused token} = \frac{\sum_{i\in \mathcal{P}}\alpha_i\mathbf{x}_i}{\sum_{i\in \mathcal{P}}\alpha_i}$ ). Consistency in reduction patterns, measured via Intersection over Area (IoA) and Normalized Mutual Information (NMI), correlates with stable performance. In diffusion models, Cached Adaptive Token Merging (CA-ToMe) uses similarity thresholds for dynamic token consolidation, complemented by a caching mechanism that leverages the smooth evolution of merging indices across timesteps (low Jaccard distance), yielding a 1.24 $\times$ speedup while maintaining FID, PSNR, SSIM metrics (Saghatchian et al., 1 Jan 2025).

5. Deduplication in Distributed and Anonymous Systems

Token deduplication in distributed systems, especially anonymous CONGEST networks, requires deterministic or randomized algorithms for collision detection. Nodes broadcast and aggregate token identifiers—using BFS tree construction and convergecast strategies—to detect duplicates efficiently, with complexity $O(D + kL/\log n)$ for deterministic settings and nearly matching lower bounds (Bai et al., 20 Aug 2024). Randomized deduplication further leverages hash-based compression with birthday-style collision probabilities, achieving correct detection in $O(D~+~k\log k/\log n)$ rounds with high probability. Minimal prior knowledge (exact $n$ or $k$ ) suffices, and practical use cases include login collision detection, distributed database conflict resolution, and distributed unique ID generation.

6. Hierarchical Token Deduplication in Mixture-of-Experts Architectures

In scale-out transformer systems with mixture-of-experts layers, token deduplication targets communication and load imbalance. HierMoE employs hierarchical token deduplication, integrating AlltoAll communication across GPU topology groupings, thereby reducing transfer (message size $n_{A2A} = G\cdot\max(p)\cdot M\cdot v$ ). Hierarchical expert swap mechanisms use cost matrices ( $Q_d$ ) to balance duplicate-free token loads, optimizing expert placement and reducing end-to-end training communication time by up to 3.32 $\times$ over Tutel-2DH, SmartMoE, and Megatron-LM baselines (Lin et al., 13 Aug 2025).

7. Challenges, Practical Considerations, and Performance Trade-offs

The efficacy of token deduplication hinges on balancing removal of true informational redundancy and preservation of task-relevant diversity. Challenges include sensitivity to hashing false positives/negatives (as with minhash LSH), internal dataset duplication, variable token quality within duplicate clusters, and the risk of excessive pruning in sequence models. Adaptive thresholding, influence estimation, and locality-aware strategies mitigate these concerns. The practical upshot is significant: optimized deduplication reduces training data size (by $\sim$ 10–20%), disk requirements, communication overhead (notably in GPU clusters), and leads to demonstrable gains in downstream model accuracy, inference speed, and system scalability (Tokpanov et al., 9 Nov 2024, Son et al., 2 Jan 2025, Lin et al., 13 Aug 2025, Wu et al., 2017).

Token deduplication is a multifaceted discipline spanning algorithmic design, statistical locality modeling, semantic token evaluation, and high-performance computing. Continued research advances not only in deduplication accuracy and efficiency, but also in its integration into end-to-end AI data pipelines, distributed systems, and large-scale LLMing contexts.