Token Initialization Methods for LLMs & Security

• Token Initialization Method is a process for generating embedding representations for new or transformed tokens, ensuring semantic transfer in both LLM adaptation and secure cryptographic settings.
• Specialized algorithms (e.g., OFA, FOCUS, HyperOfa, TokAlign, Tik-to-Tok) leverage techniques like convex combinations, sparsemax weighting, and hypernetwork mapping to optimize initialization.
• Effective initialization directly influences downstream convergence rates, loss minimization, and security metrics, driving improved outcomes in language modeling and cryptographic applications.

A token initialization method specifies how the embedding representations for new or transformed tokens are effectively created within a neural or cryptographic system when a preexisting embedding table or token domain must be extended, adapted, or securely generated. In both LLM adaptation and secure cryptographic token generation, the initialization procedure is critical for downstream convergence rate, semantic transfer, generalization, and—in security domains—unforgeability and collision resistance. Recent research advances have yielded specialized algorithms for token initialization in cross-lingual LLM expansion, cold-start recommendation, tool augmentation in LLMs, monolingual specialization, cryptographic delegation, dynamic vision transformer pruning, and vocabulary alignment.

1. Token Initialization in LLM Adaptation

Extending pretrained LLMs to new languages or domains necessitates the initialization of embeddings for previously unseen tokens introduced by specialized tokenizers. Naive random initialization disregards semantic topology and slows pre-training, whereas informed initialization methods preserve manifold locality and accelerate adaptation. Examples include:

• Similarity-based convex heuristics (OFA): Target token embeddings are set as convex combinations of $k$ source token embeddings found via nearest-neighbor search in multilingual external vector space. The embeddings are confined to the convex hull of the source tokens.
• Sparse overlapping combinations (FOCUS): For each new token, a sparsemax-weighted blend is computed over the static embeddings of overlapping source–target tokens selected by cosine similarity in an auxiliary space. This method directly builds each new token into the pretrained source manifold (Dobler et al., 2023).
• Dictionary & fastText mappings (Tik-to-Tok): One-to-many candidate source tokens are selected per target token by dictionary or fastText nearest-neighbors; a weighted mean formula assigns embedding values, enabling rapid adaptation to low-resource languages (Remy et al., 2023).
• Nonlinear hypernetwork mapping (HyperOfa): A BiLSTM-based hypernetwork, trained on aligned external word vectors and factorized source embeddings, synthesizes coordinate vectors for new tokens. These coordinates, once projected, yield the full embedding for each new token; this method can initialize tokens outside the convex hull of the source vocabulary and leverage higher-order cross-lingual correlations (Özeren et al., 21 Apr 2025).
• Vocabulary rearrangement via alignment maps (TokAlign): A one-to-one mapping matrix between source and target vocabularies is computed using GloVe co-occurrence embedding similarity, with embeddings and language modeling heads copied and reindexed to target token order. Progressive two-stage fine-tuning subsequently restores downstream model performance (Li et al., 4 Jun 2025).

2. Mathematical Formulations and Architectural Choices

The mathematical structure of token initialization varies by method:

• Convex combinations (OFA):

$e_t = \sum_{i=1}^k \alpha_i e_{s_i}$

$\alpha_i \geq 0$, $\sum \alpha_i = 1$

• Sparsemax selection (FOCUS):
  • Similarity vector $c_{a,o_i}$: cosine$(x_a, x_{o_i})$
  • Weights $w_a$ via Sparsemax; embeddings $e_a = \sum_{o \in O} w_{a,o} e^{s}_o$
• Hypernetwork mapping (HyperOfa):
  • Pool over external vectors $x_i$; output $f_\theta(x_i)$ approximating the coordinate $F^s_i$
  • Training loss combines contrastive and L1 reconstruction:

  $$L(\theta) = \lambda L_c(\theta) + (1-\lambda) L_{L1}(\theta)$$

• TokAlign alignment:

  • Mapping matrix $M_{ij}$ determined by maximum cosine similarity between GloVe token-level embeddings
  • Embedding assignment: $E_t(j) = E_s(i^*(j))$
• Tik-to-Tok weighted mean:
  • Dictionary + fastText candidates $S = [s_1, ..., s_k]$

$$\alpha_1 = 0.30 + \frac{0.6}{k}, \quad \alpha_2 = 0.10 + \frac{0.6}{k}, \quad \alpha_i = \frac{0.6}{k},\; i>2$$

$e_t = \sum_{i=1}^k \alpha_i e_{s_i}$

Hypernetwork architectures (HyperOfa) are typically multi-layer bidirectional LSTMs with large hidden dimensions, applying random shuffling for permutation invariance and severe dropout to mitigate overfitting.

3. Secure Token Initialization Algorithms

In cryptographic settings, token initialization refers to the deterministic generation of a reversible, collision-resistant token mapped from a sensitive input (e.g., PAN) using block ciphers and collision-resistant auxiliary functions. Longo–Aragona–Sala's reversible-hybrid PCI DSS-compliant algorithm (Longo et al., 2016) specifies:

• Security parameters: secret block cipher key $K$, public tweak $u$, block cipher $E$, and collision-resistant hash $f$.
• Cycle-walking: output $c = E(K, t)$ is accepted only if low-order bits encode a valid decimal token; otherwise, recursion is invoked.
• Efficiency: average $\leq 2$ AES encryptions and $\leq 1$ SHA-256 invocation per token generation.
• The scheme ensures pseudorandomness, reversibility, and resistance to brute-force and replay attacks.

4. Specialized Token Initialization for LLM Augmentation and Recommendation

New domains such as tool-augmented LLMs and cold-start collaborative filtering exploit token-level initialization for integration and semantic alignment:

• Tool token learning (TokenLearning): Each tool token's initial embedding is set as the pooled (mean/max) word-embedding vector of its name/description from the frozen LLM vocabulary. A regularization term keeps the learnable embedding close to this prior, ensuring the tool token resides semantically near related vocabulary, boosting tool call accuracy by 2–5 points across multiple benchmarks (Li et al., 17 Jun 2025).
• Cold-start recommendation (BPE-LLM): Entity metadata is BPE-tokenized, and each subword's contextualized embedding from a frozen LLM is aggregated (mean-pool or attention-weighted sum). This initialization yields substantial gains (Recall@10: 0.68 vs 0.41 for random) and supports multilingual zero-shot recommendation (Zhao et al., 16 Sep 2025).

5. Token Initialization in Dynamic Vision Transformers

Dynamic token pruning in vision transformers demands initializers that match the inference-time calculation and selection paradigm. Masked fine-tuning (Shi et al., 2023) adapts a pretrained ViT by randomly masking input tokens and training to classify using unmasked data, enhancing pruning robustness and maintaining higher Top-1 accuracy under token reduction.

• Standard pretraining does not simulate variable token counts or occlusion; masked fine-tuning does, aligning calculation patterns between training and inference.
• Hybrid masking, alternating full images and high mask ratios, yields the best balance between full-image accuracy and occlusion robustness.
• Experimental results show that initialization via masked fine-tuning reduces the loss of accuracy under strong pruning: at 0.3 keep ratio, −17.3% for MAE-MF vs. −22.9% for DeiT.

6. Comparative Performance and Impact on Downstream Tasks

Initialization methods are empirically validated for convergence speed, loss minimization, and downstream accuracy:

Method	Key Metric	Baseline	Initialized	Comments
FOCUS (Dobler et al., 2023)	MLM loss (German)	24.0	4.0	Outperforms random, matches Wechsel
HyperOfa (Özeren et al., 21 Apr 2025)	NER POS (98–369 langs)	Random < OFA	HyperOfa ≈ OFA > Random	Faster convergence, higher F1
TokAlign (Li et al., 4 Jun 2025)	Pythia 1B PPL	340	120	5k steps restore ≈97% vanilla performance
BPE LLM (Zhao et al., 16 Sep 2025)	Recall@10	0.41	0.68	20–25 pp improvement, significant p-value
Tik-to-Tok (Remy et al., 2023)	MLM loss (Frisian, 0 epoch)	9.11	5.50	SOTA for low-resource with dictionary/NN

7. Implementation Considerations and Hyperparameter Sensitivity

Implementation details are critical for reproducing reported gains:

• Hypernetwork dimension (HyperOfa): $D'=100,200,400$; best results with large BiLSTM (210M params).
• Pooling strategies: mean vs. max (TokenLearning, BPE-LLM); mean generally preferred for stability.
• Regularization: dropout (0.4), input shuffling, sparse matching.
• Fine-tuning schedules: two-stage adaptation (TokAlign), hybrid mask ratio sampling (ViT), regularization penalties (TokenLearning).
• Computational cost: token-level initialization incurs up-front expense ($O(N\cdot m \cdot L \cdot d^2)$ for BPE-LLM) but no additional online overhead.

Hyperparameter choices ($\lambda$, $\tau$, batch size, model dimension) influence results but stable ranges are identified via empirical grid search in the cited works.

8. Significance, Limitations, and Future Directions

Token initialization remains an active area due to its impact on transfer learning, domain adaptation, zero-shot generalization, and secure operation. Methods leveraging external semantic spaces, nonlinear mappings, and informed alignment exhibit strong performance—initialization errors propagate and can dramatically slow recovery or degrade downstream accuracy. Security-oriented initialization algorithms, such as those in cryptographic tokenization and agent delegation (Agentic JWT (Goswami, 16 Sep 2025)), underpin zero-trust frameworks and robust authorization protocols.

A plausible implication is that as LLMs expand to ever more languages, domains, and agents, token initialization will increasingly utilize hybrid external mappings, dynamic refinement, and distributed semantic priors. Limitations persist for rare OOV tokens, settings with non-overlapping vocabularies, and domains lacking high-quality auxiliary embeddings. The design of universal initialization strategies bridging architecture, security, and semantic alignment remains a topic for further research.