As we approach early 2025, the AI landscape continues to evolve at breathtaking speed. Yet beneath the headlines of each new model release lies a fundamental question that keeps me up at night as I build Problock AI: have we hit a wall in what pretraining alone can achieve? This question isn't merely academic—it has profound implications for the future of AI development, resource allocation, and the strategies companies employ to build the next generation of intelligent systems.

The journey of large language models from research curiosities to world-changing technologies has been marked by an almost religious faith in scaling laws—the empirical observation that bigger models trained on more data yield better performance. But recent evidence suggests we may be approaching the limits of this paradigm. In this blog, I'll unpack the technical realities behind this question, drawing on cutting-edge research and my experience scaling deep tech startups to offer insights into whether we're truly approaching fundamental limits in pretraining, or if we're simply witnessing a temporary plateau that new approaches will eventually overcome.

The Scaling Journey: From GPT-1 to Today's Frontier Models

The evolution of GPT models provides a perfect lens through which to understand the scaling phenomenon. This journey begins in 2018 with OpenAI's release of GPT-1, a transformer-based language model with 117 million parameters. While modest by today's standards, GPT-1 represented a significant advancement in natural language processing. It could generate coherent text by understanding the context of input and demonstrated the potential of pre-trained transformers.

Think of GPT-1 as a bright kindergartener—capable of basic language understanding but lacking sophisticated knowledge and reasoning. With its limited parameter count, it frequently produced text that lacked depth and consistency, much like how a child might tell a story with logical gaps and simplistic themes.

In 2019, OpenAI dramatically scaled up with GPT-2, expanding to 1.5 billion parameters—nearly 13 times larger than its predecessor. This massive increase enabled GPT-2 to generate more coherent and contextually relevant text, making it notably more capable across various NLP tasks. If GPT-1 was a kindergartener, GPT-2 was more like a talented high school student—capable of producing more sophisticated content but still prone to factual errors and logical inconsistencies.

The release of GPT-3 in 2020 marked a watershed moment. With 175 billion parameters, GPT-3 represented a 100x leap from GPT-2. This dramatic scaling brought capabilities that surprised even its creators. GPT-3 could write essays, generate code, translate languages, and even attempt reasoning tasks—all without being explicitly trained for these specific abilities. To extend our analogy, GPT-3 was like a college graduate with broad knowledge across many domains—still not an expert in any particular field, but capable of producing work that appeared professional in a wide range of contexts.

The journey continued with GPT-4, released in 2023, which further pushed the boundaries of scale and capability. While OpenAI hasn't disclosed the exact parameter count, estimates suggest it's substantially larger than GPT-3. GPT-4 demonstrated significant improvements in reasoning, knowledge retention, and specialized tasks like coding and creative writing—comparable to a seasoned professional with years of diverse experience.

More recently, OpenAI released GPT-4o, which enhanced capabilities in handling multimodal inputs and improved memory management for more coherent long-form conversations. The latest iteration, GPT-4.5 (released in February 2025), continues this progression, with improvements in pattern recognition, creative insight generation, and a more natural interaction style.

This evolution from 117 million parameters to potentially trillions represents a scaling factor of over 10,000x in just seven years—a testament to both the effectiveness of scaling as a strategy and the enormous resources dedicated to pursuing it.

The "Aha Moment": Understanding In-Context Learning and Emergent Abilities

The Surprising Phenomenon of In-Context Learning

One of the most fascinating capabilities that appeared as models scaled was in-context learning—the ability of a model to perform a new task-based solely on examples provided in the prompt, without any parameter updates. This capability revolutionised how we think about AI learning.

To understand in-context learning, imagine a chef who has never made sushi but has extensive experience with other cuisines. Show them a few examples of sushi preparation, and they can quickly grasp the patterns and produce a reasonable attempt—not because they're learning new information about cooking, but because they're applying their deep understanding of culinary principles to a new context. Similarly, large language models can adapt to new tasks "on the fly" by recognizing patterns in the examples you provide.

This capability wasn't an explicitly engineered feature; it emerged naturally as models grew larger and were exposed to more diverse training data. When OpenAI researchers first observed this behavior in GPT-3, it was a genuine "aha moment"—revealing that these models weren't just regurgitating training data but could form generalizable abstractions that transferred to novel situations.

Emergent Abilities: When Quantitative Growth Leads to Qualitative Shifts

Closely related to in-context learning is the broader concept of emergent abilities—capabilities that appear suddenly and unpredictably as models scale, often manifesting only after crossing certain size thresholds. Traditional machine learning would suggest that performance on a task should improve gradually and continuously as model size increases. However, researchers observed that some capabilities seemed to materialize almost overnight, with smaller models showing no ability whatsoever, and slightly larger models suddenly demonstrating significant proficiency.

Think of emergent abilities like the phase transition from water to ice. As you lower the temperature of water, it becomes progressively colder but remains liquid—until suddenly, at 0°C, it transforms into something qualitatively different. Similarly, as language models grow larger, they might show minimal improvement on certain tasks until they cross a threshold where the capability seemingly "crystallizes" into existence.

This phenomenon was first systematically documented by researchers who observed that models below certain parameter counts performed no better than random on tasks like logical reasoning, while models above those thresholds suddenly exhibited substantial capabilities. It's as if the models crossed an "understanding threshold" that enabled a qualitatively different kind of processing.

A New Understanding of Emergence

Recent research has begun to challenge and refine our understanding of these emergent abilities. A fascinating paper titled "Are Emergent Abilities in Large Language Models Just In-Context Learning?" suggests that what appears to be emergence might actually be a sophisticated form of in-context learning, where the model is using examples implicitly present in its training data rather than developing fundamentally new processing capabilities.

Similarly, a March 2024 paper, "Understanding Emergent Abilities of Language Models from the Loss Perspective," proposes that emergence might be better understood through the lens of pretraining loss rather than model size. This research suggests that models exhibit emergent abilities on certain tasks when their pretraining loss falls below specific thresholds, regardless of their size.

The authors demonstrate that "a model exhibits emergent abilities on certain tasks—regardless of the continuity of metrics—when its pre-training loss falls below a specific threshold. Before reaching this threshold, its performance remains at the level of random guessing". This challenges our understanding of emergence as primarily a function of scale.

To explain this with an analogy: imagine learning to ride a bicycle. Before a certain point of practice, you can't ride at all—you keep falling over. There's no partial success. Then suddenly, after enough practice, something "clicks" and you can ride. What looked like a sudden emergence of ability was actually a gradual improvement in balance and coordination that crossed a threshold where the entire system began to work. Similarly, LLMs might gradually improve their internal representations until they cross a threshold where certain abilities become possible.

Beyond Pretraining: How SFT and RLHF Transform Raw Models into Helpful Assistants

The Limitations of Raw Pretrained Models

While scaling pretraining has yielded impressive capabilities, raw pretrained models have significant limitations when deployed as assistants. They tend to be inconsistent, can generate harmful content, and often fail to follow user instructions precisely. They're like savants—extraordinarily knowledgeable but lacking in social awareness and practical judgment.

This is where techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) come in—they transform these raw intellects into helpful, harmless, and honest assistants.

Supervised Fine-Tuning: Teaching by Example

Supervised Fine-Tuning (SFT) is the first step in this transformation. It involves further training the pretrained model on carefully curated examples of desired behaviors. Think of it as showing a brilliant but socially awkward person examples of good social interactions so they can learn the patterns.

In technical terms, SFT "primes the model to respond appropriately to different user prompts". This process uses supervised learning where human annotators create examples of appropriate model responses to various prompts. The model is then fine-tuned on these examples, adjusting its parameters to better match the desired behavior patterns.

Imagine teaching someone to play tennis not by explaining the rules theoretically, but by showing them videos of excellent tennis playing and having them practice mimicking those motions. Similarly, SFT shows the model examples of good responses rather than explicitly programming rules about what makes a response good.

Reinforcement Learning from Human Feedback: Learning from Preferences

RLHF takes this process further by incorporating human judgment more directly into the training process. Rather than just providing examples, RLHF uses human evaluators to rank different possible responses. These rankings are used to train a reward model, which then guides further optimization of the language model.

To use a cooking analogy: SFT is like teaching someone to cook by giving them recipes to follow, while RLHF is like having them prepare multiple versions of a dish and then giving feedback on which tastes better, allowing them to develop an internal sense of what makes food good rather than just following instructions.

The RLHF process typically involves three key steps:

1. Training a reward model using human feedback, where humans rank different model outputs

2. Using this reward model to evaluate and score the language model's outputs

3. Fine-tuning the language model to maximize the reward score using reinforcement learning techniques

Implementation of RLHF can take various forms, as noted by Apex Data Sciences: "Implement RLHF to allow target consumers to directly evaluate the outputs they receive from your personal assistant AI. This real-world feedback helps identify areas for improvement and ensures the assistant evolves to better serve user needs".

This approach has proven remarkably effective at aligning model behavior with human preferences and values, as demonstrated by the dramatic improvement in usability from GPT-3 to ChatGPT, which employed extensive RLHF.

The Pretraining Wall: Evidence and Implications

What Are Scaling Laws and Why Do They Matter?

To understand the pretraining wall, we first need to understand scaling laws—the empirical regularities that have guided AI development in recent years. In 2020, researchers published a seminal paper demonstrating that transformer network performance improves predictably with increases in model size and dataset size.

These findings were extended in 2022 by the Chinchilla paper, which showed that "for optimal performance, every 4x increase in compute should be allocated as a 2x increase in the size of the model and dataset". These laws suggested a clear path forward: just keep scaling both models and data in the right proportion, and performance would continue to improve predictably.

Imagine scaling laws as similar to the laws of compound interest in finance—just as your investment grows predictably over time according to mathematical formulas, model performance seemed to grow predictably with increases in scale according to power laws.

Signs of a Slowdown

However, recent evidence suggests we may be approaching limits to this scaling paradigm. The blog "Scaling Laws for LLM Pretraining" notes signs of pretraining slowing down due to data bottlenecks—we're simply running out of high-quality text for training, and we aren't generating enough new data to keep pace with the voracious appetite of ever-larger models.

A recent report indicated that "AI Giants Rethink Model Training Strategy as Scaling Laws Break Down," suggesting that major players like OpenAI are encountering unexpected challenges when attempting to scale beyond certain thresholds. Some reports suggest that "OpenAI found that pretraining Orion on synthetic data made it too much like earlier models," indicating potential limitations in current approaches.

This is like a mining operation that has extracted all the easily accessible ore and now faces diminishing returns as it must dig deeper and process lower-quality material to find additional resources. The easy gains from scaling have largely been realized, and each additional improvement requires increasingly sophisticated approaches.

The Data Quality vs. Quantity Challenge

One key aspect of the pretraining wall is the trade-off between data quality and quantity. Early models could improve by simply training on more text from the internet, but as models grow larger, they quickly consume all the highest-quality text available. This forces researchers to either use lower-quality text, which can introduce biases and reduce performance, or to explore alternative data sources like synthetic data generated by other AI models—which comes with its own challenges of potential staleness and lack of novelty.

This challenge is analogous to a student who has read all the best textbooks in a field and must now turn to less reliable sources for new information. At some point, additional reading yields diminishing returns and might even introduce misconceptions.

Alternative Approaches Being Explored

In response to these challenges, researchers are exploring several alternative approaches:

1. Multimodal training: Incorporating images, video, and audio alongside text to provide richer training signals, as seen in recent models like GPT-4o.

2. Improved architectures: Developing model architectures that can learn more efficiently from the same amount of data, potentially overcoming the "critical batch size" limitations discussed in recent research.

3. Synthetic data generation: Creating high-quality synthetic data that can supplement existing datasets, though with the caution noted in OpenAI's Orion experiments.

4. Continual learning: Developing methods for models to learn incrementally from new data without forgetting what they've already learned.

These approaches represent potential paths around the pretraining wall, but each comes with its own technical challenges and limitations.

Key Research Papers: Insights into the Scaling Challenge

Several recent papers provide critical insights into the challenges and potential solutions regarding the pretraining wall:

"Understanding Emergent Abilities of Language Models from the Loss Perspective" (March 2024)

This paper proposes a novel approach to studying emergent abilities by focusing on pretraining loss rather than model size or training compute. The authors demonstrate that models with the same pretraining loss generate similar performance on downstream tasks, regardless of differences in model and data sizes.

What makes this paper particularly interesting is its finding that emergent abilities appear when a model's pretraining loss falls below specific thresholds. Before reaching these thresholds, performance remains at random-guessing levels, but once crossed, performance improves dramatically. This suggests that what we perceive as emergent abilities might be better understood as threshold effects in learning dynamics.

If this hypothesis proves true, it would mean that rather than blindly scaling models, we could focus more precisely on reducing pretraining loss through targeted architectural improvements or data selection strategies. This would be like discovering that athletic performance depends not on how many hours you train (quantity) but on whether your heart rate reaches certain zones during training (quality)—a much more efficient approach.

"Scaling Laws for Pre-training Agents and World Models" (November 2024)

This research extends scaling law analysis to embodied agents and world models, showing that power laws similar to those found in language modeling also apply to these domains. However, the paper highlights that "the coefficients of these laws are heavily influenced by tokenizers, tasks, and architectures."

The significance of this work lies in its demonstration that scaling laws are both pervasive across AI domains and highly context-dependent. This suggests that while we may be approaching limits in certain contexts (like text-only pretraining), other modalities or architectural approaches might still have substantial room for scaling improvements.

The authors note: "Going beyond the simple intuition that 'bigger is better', we show that the same types of power laws found in language modeling also arise in world modeling and imitation learning". This insight could help researchers identify which domains and approaches still have significant headroom for scaling.

"Scaling Pre-training to One Hundred Billion Data for Vision Language Models" (February 2025)

This very recent paper investigates pretraining vision-language models on an unprecedented scale: 100 billion examples. Interestingly, the researchers found that "model performance tends to saturate at this scale on many common Western-centric benchmarks."

However, the paper also reports that "tasks of cultural diversity achieve more substantial gains from the 100-billion scale web data, thanks to its coverage of long-tail concepts". Similarly, low-resource languages show continued improvement at scales where high-resource languages plateau.

This research highlights a nuanced reality: while we may be hitting walls for certain mainstream tasks and high-resource languages, scaling continues to yield benefits for representing diversity and serving underrepresented communities. It's like discovering that additional practice no longer improves performance in common sports but continues to yield benefits in less popular ones—a finding with significant implications for building inclusive AI systems.

My Hypotheses: Where Do We Go From Here?

Based on the current research landscape and my experience scaling AI systems at Problock AI, I've developed several hypotheses about the future of large language models and how we might address the pretraining challenge:

Hypothesis 1: The Multimodal Escape Hatch

I believe that while text-only pretraining may be approaching fundamental limits, multimodal pretraining still has substantial room for scaling. Text represents only one way humans communicate and understand the world; incorporating images, video, audio, and even interactive environments could provide much richer training signals.

This is similar to how a person might hit a plateau when learning a language through textbooks alone, but make rapid progress when immersed in a cultural environment where they see, hear, and interact with the language in context. The additional modalities provide complementary information that helps resolve ambiguities and build richer representations.

If this hypothesis is correct, we should expect the next generation of breakthrough models to be fundamentally multimodal, with text capabilities enhanced by training jointly across modalities rather than in isolation. This would represent not just a technical evolution but a conceptual shift in how we think about language understanding—moving from text as the primary medium to text as one component of a richer communicative ecosystem.

Hypothesis 2: The Return of Architecture Innovation

During the height of the scaling era, architectural innovation took a back seat to simply making models bigger. My hypothesis is that as scaling alone yields diminishing returns, we'll see a renaissance in architectural innovation focused on more efficiently extracting knowledge from data.

This might include attention mechanisms that scale sub-quadratically, more sophisticated approaches to memory and retrieval, and architectures specifically designed to leverage multimodal data. The paper on "How Does Critical Batch Size Scale in Pre-training?" already points in this direction, suggesting that understanding the relationship between batch size, dataset size, and model size could lead to more efficient training approaches.

To use an analogy: when gasoline was cheap and abundant, car manufacturers focused on making bigger, more powerful engines. When fuel became expensive and limited, innovation shifted to creating more efficient engines and hybrid technologies. Similarly, as the easy gains from scaling are exhausted, I expect innovation to shift toward architectural efficiency.

Hypothesis 3: From Passive to Active Learning

Current pretraining is largely passive—models absorb whatever information is in their training data. I hypothesize that future breakthroughs will come from more active approaches to learning, where models can query, explore, and interact with their environment to resolve uncertainties and build more robust knowledge representations.

This shift would mirror how humans learn—not just by reading texts, but by asking questions, conducting experiments, and testing hypotheses. Consider how a child learns: they don't just passively absorb information; they constantly ask "why?" and experiment with their environment to validate their understanding.

Developments in RLHF point in this direction, with models like OpenAI's o1-preview and o1-mini designed to "spend more time thinking before they respond". These models represent a step toward more active, deliberative reasoning rather than passive pattern matching.

Hypothesis 4: Specialized Models Will Outperform Generalists in Key Areas

While the trend has been toward increasingly large, general-purpose models, I believe we'll see a counter-trend toward smaller, specialized models that outperform much larger generalists in specific domains. These specialized models would be trained on carefully curated domain-specific data and might employ architectures tailored to their particular tasks.

OpenAI's release of o3-mini, described as their "newest cost-efficient reasoning model optimized for coding, math, and science," suggests this trend is already underway. This specialization is analogous to how human expertise develops—while we all have general intelligence, we become truly exceptional in specific domains through specialized training and experience.

If this hypothesis proves correct, we might see an ecosystem of complementary models rather than a single monolithic system—similar to how human expertise is distributed across specialists rather than concentrated in generalists.

Conclusion: Not a Wall, But a Changing Landscape

So, have we hit a pretraining wall? The evidence suggests we're not facing an absolute barrier but rather diminishing returns from the simplest approach to scaling. The low-hanging fruit of bigger models and more data has largely been picked, and we're now entering a phase where progress requires more sophisticated approaches.

This transition isn't unprecedented—it parallels previous shifts in AI research, where periods of rapid progress through straightforward scaling were followed by plateaus that eventually gave way to conceptual breakthroughs. The shift from simple perceptrons to multi-layer networks, or from hand-crafted features to learned representations, followed similar patterns.

At Problock AI, we're exploring multiple paths forward—investigating multimodal training, experimenting with novel architectures, and developing specialized models for key domains. We believe that while the era of easy scaling may be coming to an end, the possibilities for AI advancement remain vast.

The pretraining challenge doesn't mark the end of AI progress but rather the beginning of a new, more sophisticated phase—one that will likely yield systems that are not just more powerful but more efficient, more diverse in their capabilities, and ultimately more useful to the humans they're designed to serve.

For those interested in diving deeper into these topics, I recommend starting with the papers on understanding emergent abilities from the loss perspective, scaling laws for pretraining agents, and the fascinating work on scaling vision-language pretraining to 100 billion examples. These cutting-edge research directions point to a future where AI progress continues not through brute-force scaling but through deeper understanding of the fundamental principles that govern learning in complex systems.

The journey continues, and the most exciting developments may still lie ahead.

Until next time,

- Tanay