# NDP: Next Distribution Prediction as a More Broad Target

arXiv: https://arxiv.org/abs/2408.17377 · HTML: https://mlsanity.com/paper/2408.17377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://mlsanity.com/paper/2406.08464/) (2024)
- [LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks](https://mlsanity.com/paper/2402.01817/) (2024)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [Aloe: A Family of Fine-tuned Open Healthcare LLMs](https://mlsanity.com/paper/2405.01886/) (2024)

## Built on by (most cited first, 2 of 2)

- [GRAM: A Generative Foundation Reward Model for Reward Generalization](https://mlsanity.com/paper/2506.14175/) (2025)
- [Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale](https://mlsanity.com/paper/2604.18572/) (2026)

## Abstract (arXiv)

Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities. However, the existing NTP paradigm contains several limitations, particularly related to planned task complications and error propagation during inference. In our work, we extend the critique of NTP, highlighting its limitation also due to training with a narrow objective: the prediction of a sub-optimal one-hot distribution. To support this critique, we conducted a pre-experiment treating the output distribution from powerful LLMs as efficient world data compression. By evaluating the similarity between the n-gram distribution and the one-hot distribution with LLMs, we observed that the n-gram distributions align more closely with the output distribution of LLMs. Based on this insight, we introduce Next Distribution Prediction (NDP), which uses n-gram distributions to replace the one-hot targets, enhancing learning without extra online training time. We conducted experiments across translation, general task, language transfer, and medical domain adaptation. Compared to NTP, NDP can achieve up to +2.97 COMET improvement in translation tasks, +0.61 average improvement in general tasks, and incredible +10.75 average improvement in the medical domain. This demonstrates the concrete benefits of addressing the target narrowing problem, pointing to a new direction for future work on improving NTP.