In the ever-evolving realm of artificial intelligence (AI), certain fundamental principles have guided the development and advancement of AI models. Just as we have basic laws of physics that govern the natural world, the field of AI was once governed by a simple, overarching concept: that increasing computational power, expanding training datasets, and adding more parameters would result in a superior AI model. However, as AI technology has progressed, this notion has expanded into three distinct laws that better describe the relationship between computational resources and model performance. These are known as the AI scaling laws: pretraining scaling, post-training scaling, and test-time scaling, also known as "long thinking." These laws illustrate how AI has adapted to leverage computational power across increasingly complex applications.
Understanding Pretraining Scaling
Pretraining scaling is the original concept that laid the foundation for AI development. It indicates that by enlarging the dataset size, increasing the number of model parameters, and utilizing more computational resources, developers could achieve predictable advancements in AI model intelligence and accuracy. These three elements—data, model size, and compute—are intrinsically linked. The pretraining scaling law suggests that when larger models are provided with more data, their overall performance improves. However, achieving this requires scaling up computational resources to handle the larger workloads, hence the need for powerful computing systems.
This principle of pretraining scaling has led to the creation of extraordinarily large models that possess groundbreaking capabilities. It has also driven significant innovations in model architecture, leading to the development of billion and trillion-parameter transformer models, mixture of experts models, and innovative distributed training techniques—all of which demand considerable computational power. The relevance of pretraining scaling continues as we produce more and more multimodal data, including text, images, audio, video, and sensor information, all of which will be instrumental in training future AI models.
Exploring Post-Training Scaling
The task of pretraining a large foundation model is not something every organization can undertake due to the substantial investment, expertise, and data required. However, once a model is pretrained and made available, it becomes a valuable resource that others can adapt for their own specific applications, thereby lowering the barrier to AI adoption. This process is known as post-training scaling, and it creates a cumulative demand for accelerated computing across enterprises and the broader developer community. Popular open-source models can lead to hundreds or even thousands of derivative models tailored for various domains.
The ecosystem of derivative models for different use cases can require up to 30 times more computational resources than it took to pretrain the original foundation model. This post-training scaling law implies that a pretrained model’s performance can be enhanced in terms of computational efficiency, accuracy, or domain specificity using various techniques such as fine-tuning, pruning, quantization, distillation, reinforcement learning, and synthetic data augmentation.
- Fine-tuning: This involves using additional training data to customize an AI model for specific domains and applications. It can be done using internal datasets or pairs of sample model inputs and outputs.
- Distillation: Involves a large, complex teacher model and a smaller student model. The student model learns to emulate the outputs of the teacher model, usually through a process known as offline distillation.
- Reinforcement Learning (RL): This machine learning technique uses a reward model to train an agent to make decisions that align with a specific use case. The goal is to maximize cumulative rewards over time, such as a chatbot receiving positive feedback from users. Reinforcement learning from human feedback (RLHF) and reinforcement learning from AI feedback (RLAIF) are examples of this technique.
- Best-of-n Sampling: This generates multiple outputs from a language model and selects the one with the highest reward score based on a reward model, improving AI outputs without changing model parameters.
- Search Methods: These explore various decision paths before selecting a final output, iteratively enhancing the model’s responses.
To support post-training, developers can use synthetic data to augment real-world datasets, enhancing the model’s ability to handle unique edge cases not adequately represented in the original training data.
Delving into Test-Time Scaling
Large language models (LLMs) are known for their ability to generate rapid responses to input prompts. While effective for straightforward questions, this approach may fall short when dealing with complex queries. In such cases, the process of test-time scaling, or long thinking, comes into play. This involves applying additional computational effort during inference to enable the model to reason through multiple potential responses before arriving at the best answer.
This technique is akin to human reasoning; for simple arithmetic like adding two plus two, a person quickly provides an answer. However, for more complex tasks like developing a business plan, a person would need to think through various options and provide a multistep answer. Similarly, AI models using test-time scaling can explore different solutions and break down complex requests into multiple steps, often showing their reasoning process to the user.
The test-time compute methodology encompasses several approaches, including:
- Chain-of-thought Prompting: Breaking down complex problems into simpler, manageable steps.
- Sampling with Majority Voting: Generating multiple responses to the same prompt and choosing the most frequently occurring answer as the final output.
- Search: Exploring and evaluating multiple paths within a tree-like structure of responses.
Post-training methods like best-of-n sampling can also be utilized during inference to optimize responses based on human preferences or other objectives.
How Test-Time Scaling Empowers AI Reasoning
The advent of test-time compute has unlocked the potential for AI to provide well-reasoned, accurate responses to complex, open-ended user queries. These capabilities are crucial for tasks requiring detailed, multistep reasoning, expected of autonomous agentic AI and physical AI applications. Across various industries, these advancements could enhance efficiency and productivity by offering users highly capable assistants to expedite their work.
In healthcare, AI models could leverage test-time scaling to analyze vast datasets, infer disease progression, predict complications from new treatments, or suggest clinical trials based on an individual’s disease profile, providing a detailed reasoning process for each recommendation. In retail and logistics, long thinking can aid in decision-making processes to address operational challenges and strategic goals, enabling more accurate demand forecasting, optimized supply chain routes, and sustainable sourcing decisions.
For global enterprises, AI reasoning models can draft comprehensive business plans, generate complex code, or optimize travel routes for delivery vehicles and autonomous systems. AI reasoning models are rapidly evolving, with new models like OpenAI’s o1-mini and o3-mini, DeepSeek R1, and Google DeepMind’s Gemini 2.0 Flash Thinking emerging recently.
These models require significantly more computational resources to perform reasoning during inference, necessitating that enterprises scale their computing capabilities to support the next generation of AI reasoning tools capable of complex problem-solving and planning.
For more insights into how NVIDIA AI can accelerate inference, visit their website.
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