Token Order Prediction (TOP) in Language Models

• Token Order Prediction (TOP) is an auxiliary training objective that ranks upcoming tokens by their proximity, enhancing a model’s understanding of future context.
• It employs a listwise ranking loss that integrates seamlessly with the transformer backbone using minimal additional parameters compared to traditional NTP and MTP.
• Empirical evaluations show that TOP improves downstream benchmarks by reducing test perplexity and enhancing generalization, making it a scalable pretraining strategy.

Token Order Prediction (TOP) is an auxiliary LLM training objective that enhances next-token modeling by explicitly imposing an ordering over future tokens. Unlike traditional next-token prediction (NTP)—which requires the model to predict the immediate next token—TOP leverages a learning-to-rank loss to optimize the model for understanding and ranking the proximity of upcoming tokens, thereby structuring the output to better align with future context. This approach has been empirically shown to achieve consistent improvements in downstream LLMing benchmarks compared to both NTP and Multi-Token Prediction (MTP) at scale, marking a substantial contribution to model pretraining efficiency and generalization.

1. Conceptual Distinction from NTP and MTP

Token Order Prediction is defined by its contrast to two prevalent objectives:

• Next-Token Prediction (NTP): Models are trained to predict $x_{t+1}$ given context $x_1, \dots, x_t$. The loss is calculated as the negative log-likelihood of the true next token under the model’s softmax output head.
• Multi-Token Prediction (MTP): Models attempt to predict several future tokens ($x_{t+1}$, $x_{t+2}$, …) typically using separate transformer layers for each future position. While MTP increases sample efficiency and can facilitate faster generation or inductive reasoning circuits, it often struggles with training instability and yields inconsistent gains on generic NLP benchmarks, especially at larger scale.
• Token Order Prediction (TOP): Rather than requiring exact prediction of each future token, TOP tasks the model to learn a ranking over the set of upcoming tokens according to their proximity. The loss optimizes for the model’s ability to assign highest scores to the tokens that will appear soonest, introducing a listwise learning-to-rank objective.

This shift in auxiliary training from strict prediction to ranking is argued to be both more attainable and more synergistic with the practical challenges of LLM pretraining.

2. TOP Learning-to-Rank Loss Formulation

The methodological centerpiece of TOP is a listwise ranking loss inspired by ListNet. For each position $t$ in the input sequence, the model constructs a ranking vector $y_{t}$ over the vocabulary, populated with nonzero proximity scores for those tokens that occur in the subsequent window of text. The ranking vector is “soft” rather than one-hot, capturing the ordering among the next $W$ tokens:

$$L_{TOP} = - \sum_{t=1}^{T} \mathrm{softmax}(y_{t}) \cdot \log\left( \mathrm{softmax}(u_{TOP}(h^{L}_{t})) \right),$$

where:

• $h^{L}_{t} \in \mathbb{R}^{D}$ is the final hidden state of the Transformer at position $t$,
• $u_{TOP}$ is a linear mapping (unembedding) from hidden states to vocabulary logits,
• $\mathrm{softmax}(y_{t})$ induces a probability distribution that emphasizes tokens closest in the future.

Crucially, only one additional unembedding layer $u_{TOP}$ is added to the architecture for TOP, whereas MTP requires separate transformer blocks (or heads) per future position.

At each training step, the overall loss combines the original NTP and the auxiliary TOP loss: $L = L_{NTP} + L_{TOP}$ TOP’s ranking loss is computationally lightweight and scales favorably even as window size $W$ increases, since the main transformer trunk is shared throughout.

3. Model Architecture and Implementation

TOP’s implementation does not require heavy architectural changes. Both NTP and TOP leverage the same transformer backbone; only the output heads differ, and both heads can operate concurrently given the shared representation. The following summarizes the architectural distinction:

Objective	Additional Parameters	Output Heads	Training Overhead	Inference Cost
NTP	None	Single	None	None
MTP	Extra transformer layers per future token	Multiple	High	None (main head only)
TOP	One linear unembedding layer	NTP + TOP head	Minimal	None (main head only)

During inference, only the NTP head is used, ensuring that deployment latency is unaffected by the auxiliary objective.

4. Comparative Experimental Evaluation

Comprehensive experiments were conducted at 340M, 1.8B, and 7B parameter scales, with models trained on the FineWeb-Edu “sample-100BT” subset (52B tokens for the smallest and 104B for larger models) and evaluated on eight standard NLP benchmarks: Lambada, HellaSwag, ARC Challenge, PIQA, SciQ, Social IQa, NaturalQuestions Open, and TriviaQA.

Key findings:

• TOP models uniformly outperformed both baseline NTP and MTP models on aggregate scores, perplexity, and accuracy across all evaluation datasets.
• While TOP led to a slightly higher training loss on the NTP head, it achieved better generalization as evidenced by lower test perplexity and higher benchmark scores, suggesting possible regularization benefits.
• MTP offered only sporadic improvements (notably on coding/generative tasks), but was unstable or performed worse than TOP/NTP on complex benchmarks and larger model scales.

5. Practical Implications

TOP introduces a scalable auxiliary look-ahead objective that indirectly promotes more robust internal representations for downstream tasks:

• Regularization: The listwise ranking structure of TOP may help prevent overfitting on the training corpus through a “soft” supervision signal that enables the network to learn long-range dependencies without the instability of hard multi-token classification.
• Parameter Efficiency: Only one linear output head is needed, allowing larger ranking windows with minimal architectural impact.
• Zero inference overhead: The NTP-only inference regime allows pretrained models to benefit from TOP’s auxiliary signal during training without any performance or resource penalty at deployment.

Potential applications include improved next-token prediction, enhanced look-ahead reasoning, flexible LLM pretraining, and regularization for generative models.

6. Limitations and Directions for Future Research

Despite TOP’s empirical success, several limitations and open questions remain:

• While TOP improves standard NLP benchmarks, its efficacy on generative tasks (e.g., summarization, code synthesis) has yet to be robustly validated.
• The optimal window size $W$ for ranking remains an open hyperparameter, subject to further investigation.
• TOP’s potential for accelerating inference via speculative decoding—by leveraging improved look-ahead context—warrants deeper exploration.
• Comparative analysis with alternative MTP variants (e.g., DeepSeek V3), and synthetic tasks (star graph navigation) may further elucidate its boundaries and strengths.

Expanding TOP’s scope to new domains (such as vision or structured data modeling) and integrating it with more sophisticated architecture adaptations may lead to further performance gains and insights into token order modeling.

7. Significance within the LLMing Paradigm

Token Order Prediction represents a paradigm shift in auxiliary LLM objectives, departing from strict autoregressive or multi-token generation tasks toward a learning-to-rank formulation that is both tractable and generalizable. It bridges the gap between Markovian NTP objectives and more global sequence modeling, furnishing models with a structured sense of future context. By doing so, it promises both efficiency and improved generalization as models continue to scale.

TOP’s introduction and comprehensive empirical validation suggest that future directions in model pretraining should consider ranking-based auxiliary losses as an alternative or complement to next-token and multi-token prediction objectives, particularly when targeting robust performance across heterogeneous NLP benchmarks (Zuhri et al., 26 Aug 2025).