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Pre-Training GPT-4.5
@ OpenAI
04/11/25
Takeaways
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Sam Altman
04/11/25
@ OpenAI
When we launched GPT-4.5, we were proud of the model, but users liked it much more than we anticipated, expressing that it was significantly better than GPT-4 in various ways.
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OpenAI Cast
04/11/25
@ OpenAI
Creating a giant model like GPT-4.5 requires a lot of people, time, and computational resources.
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OpenAI Cast
04/11/25
@ OpenAI
The project for GPT-4.5 began about two years ago, anticipating a new cluster coming online, which led to extensive planning and risk management before the actual run.
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OpenAI Cast
04/11/25
@ OpenAI
The process of developing a model involves collaboration between machine learning and systems teams, from inception to the training run, often facing unresolved issues at launch.
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OpenAI Cast
04/11/25
@ OpenAI
We often enter a launch with many unresolved issues, trying to make progress despite challenges by adding more compute and addressing unforeseen problems.
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OpenAI Cast
04/11/25
@ OpenAI
The initial goal for GPT-4.5 was to create a model that is ten times smarter than GPT-4, which we ultimately achieved through a complex process.
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OpenAI Cast
04/11/25
@ OpenAI
Scaling from 10,000 GPUs to 100,000 GPUs introduces many issues, as rare occurrences can become catastrophic at larger scales, affecting infrastructure and failure rates.
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OpenAI Cast
04/11/25
@ OpenAI
Training large models is challenging because new issues arise that weren't anticipated at the project's inception, requiring ongoing adjustments and problem-solving.
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OpenAI Cast
04/11/25
@ OpenAI
The GPT architecture excels at efficiently absorbing and generalizing data, but as compute resources grow, data becomes the bottleneck, necessitating algorithmic innovations to learn more from the same amount of data.
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OpenAI Cast
04/11/25
@ OpenAI
The sheer volume of work required for GPT-4.5 was a byproduct of the model's specifications, necessitating changes that couldn't be achieved with the same stack used for GPT-4.
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OpenAI Cast
04/11/25
@ OpenAI
The early phases of a new generation of hardware often present significant failure rates, which improve as the team learns to address new failure modes during the training process.
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OpenAI Cast
04/11/25
@ OpenAI
We are no longer in a compute-constrained environment for our best models, marking a significant shift in our approach to model training and development.
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OpenAI Cast
04/11/25
@ OpenAI
We've learned a lot through the process of training this model, discovering what scales well and what doesn't.
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OpenAI Cast
04/11/25
@ OpenAI
The defining characteristics of the GPT paradigm are that you can predict the test loss, and it scales predictably, leading to greater intelligence in various intangible ways.
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OpenAI Cast
04/11/25
@ OpenAI
In testing GPT-4.5, we found it has nuanced abilities that were unexpected, indicating it is more intelligent in ways that are hard to characterize ahead of time.
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OpenAI Cast
04/11/25
@ OpenAI
User satisfaction shows that GPT-4.5 is smarter in subtle ways, demonstrating more common sense and understanding of nuance and context.
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OpenAI Cast
04/11/25
@ OpenAI
During the training run, we made changes that had a positive impact, sometimes better than anticipated, which was an exciting moment for the team.
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OpenAI Cast
04/11/25
@ OpenAI
The effort to parallelize work aggressively during the run was crucial to avoid performance cliffs that could make the model untrainable.
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OpenAI Cast
04/11/25
@ OpenAI
Resolving issues during the training run led to a significant performance boost, which energized the team and improved morale.
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OpenAI Cast
04/11/25
@ OpenAI
The ML code design didn't stop at launch; the team continued to work on issues and improvements actively, fostering a powerful spirit of teamwork.
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OpenAI Cast
04/11/25
@ OpenAI
Despite challenges, we had extensive planning for the run, starting a year before training began, which included careful sequencing of changes to minimize risks.
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OpenAI Cast
04/11/25
@ OpenAI
We had to be very paranoid about scaling laws, ensuring that any wins from new features were persistent across different scales.
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OpenAI Cast
04/11/25
@ OpenAI
A bug in the torch sum function was discovered, which had been causing various symptoms, and fixing it resolved multiple issues at once, leading to a fun moment for the team.
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OpenAI Cast
04/11/25
@ OpenAI
Post-launch, the team continuously monitored the run for improvements and correctness, balancing the need for data efficiency with the challenges of noisy signals.
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OpenAI Cast
04/11/25
@ OpenAI
There's a lot of optimism about achieving better data efficiency in AI, as the field is entering a new stage where stacking small improvements can lead to significant gains.
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OpenAI Cast
04/11/25
@ OpenAI
While a 10 million GPU synchronous pre-training run may not look like current methods, there will likely be some form of training run at that scale in the future, and I would call the learning process semi-synchronous, as it involves many GPUs working together on an AI system, but not all parts will necessarily communicate with each other.
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OpenAI Cast
04/11/25
@ OpenAI
Pre-training and unsupervised learning enhance the model's broad-based intelligence and improve its generalization capabilities, complementing reasoning abilities that can be more variable.
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OpenAI Cast
04/11/25
@ OpenAI
Pre-training is inherently broad and diverse, making it challenging to achieve the same breadth when using reinforcement learning in specific environments.
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OpenAI Cast
04/11/25
@ OpenAI
Pre-training compresses data by identifying connections and analogies, while reasoning focuses on specific problems, requiring careful thought to unlock various problem-solving skills.
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OpenAI Cast
04/11/25
@ OpenAI
The bottleneck in scaling AI systems can vary, but optimizing code design can help adapt workloads to the existing infrastructure, balancing resource demands effectively.
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OpenAI Cast
04/11/25
@ OpenAI
The collaboration between teams on model specifications for GPT-4.5 involved deep co-design efforts, focusing on optimizing functionality and ensuring the ML and systems work well together at scale.
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OpenAI Cast
04/11/25
@ OpenAI
The emphasis on co-design in this project was crucial, as it ensured that the system's properties emerged from a well-steered design process rather than occurring by chance.
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OpenAI Cast
04/11/25
@ OpenAI
The practice of building systems involves reconciling idealized views of how things should work with the practical realities of existing technology and infrastructure.
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OpenAI Cast
04/11/25
@ OpenAI
The scaling laws observed in AI suggest that larger models trained longer lead to more compression and intelligence, which is supported by the philosophical grounding of these concepts.
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OpenAI Cast
04/11/25
@ OpenAI
The long tail of relevant concepts in data means that simply gathering data passively requires significantly more compute to achieve incremental improvements in model performance.