The Evolving Landscape of AI Training Methodologies

The development of AI is undergoing a rapid transformation, driven by advancements in training methodologies. These approaches define how AI models evolve from general-purpose language processors to highly refined tools tailored to user needs. At the heart of this evolution are pre-training and post-training, two distinct phases that shape an AI’s capabilities.

Pre-training establishes a broad understanding of language and factual knowledge, while post-training refines AI models for specific tasks, improving alignment with human preferences. As the costs of pre-training rise and efficiency gains diminish, post-training has emerged as the more cost-effective and impactful phase of AI development.

A crucial consequence of this shift is that AI development no longer requires massive, general-purpose models alone. Instead, smaller, more targeted models can now be built on top of existing pre-trained architectures through efficient post-training techniques. This shift is reshaping AI strategy, making AI more accessible and cost-effective across industries.

Pre-Training: The Foundation of Language Models

Pre-training is the initial phase where a model learns to predict the next token in a vast dataset. This process allows the model to develop a fundamental grasp of grammar, semantics, and factual information.

Modern AI models train on datasets containing trillions of words, requiring enormous computational resources. Training runs typically span weeks or months, leveraging thousands of GPUs and TPUs to process data at an immense scale. To optimise efficiency, developers employ scaling strategies, such as sparse training, where only a subset of model parameters is activated at any given time. Techniques like Mixture of Experts (MoE) reduce computational costs by distributing tasks among different model components. Parameter-efficient fine-tuning, such as Low-Rank Adaptation (LoRA), allows large models to adapt to new tasks with minimal computational overhead.

Despite its effectiveness, pre-training is now facing diminishing returns. The early leaps from GPT-2 to GPT-3 yielded massive performance improvements, but more recent upgrades, such as GPT-4 and Claude 3, have only shown incremental gains despite exponentially increasing training costs.

Additionally, access to high-performance computing resources remains highly unequal. The cost of pre-training a cutting-edge model can exceed $100 million, making it feasible only for tech giants such as OpenAI, Google DeepMind, and Anthropic. This raises the question: Is full-scale pre-training still the best investment for AI development?

Post-Training: Refining AI for Real-World Use

Once pre-training is complete, the model enters the post-training phase, where it is fine-tuned to generate more accurate, coherent, and human-aligned responses. This stage is crucial in transforming a general-purpose model into a practical AI tool.

One of the most effective post-training techniques is instruction tuning, also known as supervised fine-tuning (SFT). This method involves training the model on carefully curated datasets containing prompts and corresponding responses, ensuring it follows instructions more effectively. OpenAI and Anthropic refine their models using this approach, improving structure, coherence, and factual accuracy in responses.

Another key technique is reinforcement learning from human feedback (RLHF). This process involves human evaluators ranking AI-generated responses based on clarity, relevance, and helpfulness. The model is then optimised through reinforcement learning to favour highly rated outputs. While RLHF has significantly improved AI alignment, it also introduces potential biases, as human feedback can reflect subjective cultural, political, or ideological preferences.

The Rise of Smaller, Targeted AI Models

Post-training is now enabling a fundamental shift away from the idea that bigger is always better in AI. Instead of continuously training massive, general-purpose models, AI labs and enterprises are increasingly focusing on smaller, more efficient models that leverage pre-trained architectures.

• Domain-Specific AI: Fine-tuning allows organisations to create models tailored to medicine, finance, law, and other industries without retraining a foundation model from scratch.
• Lighter, More Cost-Effective AI: By applying post-training to open-weight models like Mistral-7B or LLaMA-3, businesses can develop highly capable models without requiring supercomputer-scale infrastructure.
• Adaptability & Customisation: Companies can train models on proprietary datasets, making AI more responsive to business needs without the risks of relying on a massive black-box AI.

This marks a fundamental shift in AI strategy—rather than chasing bigger models, the focus is now on smarter fine-tuning.

The ROI Shift: Why Post-Training is Now the Superior Investment

For most organisations, particularly those without access to supercomputing resources, investing in post-training is now a more practical and cost-effective approach.

Diminishing Returns in Pre-Training

• Training a model like GPT-4 costs over $100 million, yet its improvements over GPT-3.5 are marginal compared to earlier advancements.
• Each doubling of model size provides smaller efficiency gains, making further large-scale pre-training increasingly unsustainable.
• AI labs that once focused on scaling up models are now prioritising efficiency improvements through post-training techniques.

The Cost-Effectiveness of Post-Training

• Fine-tuning an existing model costs a fraction of full-scale pre-training while delivering meaningful improvements in alignment, reasoning, and usability.
• Post-training can be completed in weeks, compared to months or years for pre-training, accelerating AI deployment.
• The ability to customise AI models for specific industries (e.g., law, healthcare, finance) through post-training fine-tuning makes it a better investment for most enterprises.

The industry-wide shift towards post-training as the primary method for AI improvement is driving the development of smaller, more adaptable AI models, moving away from the old paradigm of endlessly increasing model size.

So, what does the future hold?

Pre-training and post-training remain foundational pillars of AI development, but their relative value has shifted. While pre-training was once the primary driver of AI advancements, its escalating costs and diminishing efficiency gains have made post-training the more viable investment.

As AI research progresses, post-training is poised to become the dominant methodology for refining and deploying AI at scale. Rather than chasing marginal improvements through costly pre-training, the future of AI will likely be shaped by increasingly sophisticated post-training techniques that extract maximum value from existing architectures—offering a more sustainable and efficient path forward.

Crucially, this shift enables the rise of smaller, more targeted AI models that can be fine-tuned for specific needs rather than relying on monolithic general-purpose models. This is a fundamental change in AI strategy, making the technology more accessible, adaptable, and cost-effective for real-world applications.

First published on Curam-Ai