Pre-Training GPT-4.5

The discussion centers on the development and launch of GPT-4.5, highlighting the extensive research, planning, and challenges involved in creating advanced AI models. Key team members, Alex, Amin Chian, and Dan, detail the collaborative process between machine learning and systems architecture, emphasizing the complexity of scaling operations and the unexpected issues that arise. They explore the importance of data efficiency, system co-design, and the ongoing quest for more intelligent models. The conversation also touches on the philosophical and technical aspects of AI scaling laws, illustrating the continuous evolution in AI research and development.

• The podcast focuses on discussing the research and development process behind GPT-4.5, a significant advancement from GPT-4.
• The team was surprised by the overwhelmingly positive reception of GPT-4.5, which was perceived as significantly better than its predecessor.
• The development of GPT-4.5 involved extensive planning, derisking runs, and collaboration across multiple teams over approximately two years.
• The project aimed to create a model that was 10 times smarter than GPT-4, requiring a massive computational effort and coordination.

"When we launched GPT-4.5, we thought people were going to like it. We were very proud of the model, but people liked it much more than we thought."

• The team was taken aback by the positive reception and the perceived improvements over GPT-4.

"We started this project basically two years ago or so, and we kind of knew that we had a big new cluster coming online."

• The planning and development of GPT-4.5 began with foresight into new computational resources.

• Scaling up from smaller models to larger ones like GPT-4.5 introduces numerous unforeseen challenges, particularly in system infrastructure and failure rates.
• The process involved balancing the resolution of known issues with the flexibility to address unknowns as they arose.
• The execution phase required a concerted effort from a large team to manage the training process and overcome unexpected obstacles.

"We almost always go into a launch with a lot of unresolved issues and try to make forward progress throughout the run despite all the challenges."

• The team had to navigate unresolved issues and adapt during the execution phase to ensure progress.

"It's always a balance of figuring out not to delay the process unreasonably."

• The team had to decide between delaying the launch to resolve issues or proceeding and addressing them on the go.

• Training large models like GPT-4.5 is inherently complex due to the scale, requiring both algorithmic and system-level innovations.
• The transition from smaller to larger computational resources, such as GPUs, complicates the process due to increased failure rates and infrastructure challenges.
• Despite the complexity, past experiences and improvements in systems have made retraining smaller models like GPT-4 more manageable.

"Issues with the infrastructure, the failure rates that you observe, the variety of failures that you observe, in both in terms of the types of failures and also the count itself."

• Infrastructure and failure rates are significant challenges in scaling up model training.

"It took like hundreds of people almost all of OpenAI's effort to do GPT-4.5."

• The development of GPT-4.5 required a massive organizational effort.

• Future scaling of models will require improvements in data efficiency and algorithmic innovations to overcome bottlenecks.
• System-level changes, such as multicluster training and state management, were necessary for GPT-4.5 and will continue to be crucial as models scale further.
• The team anticipates that achieving the next level of advancement will involve overcoming new challenges and making strategic compromises.

"The transformer, the GPT, is spectacular at making productive use of data, but there's a ceiling to how deep of an insight it can gain from the data."

• The efficiency of data use is a current limitation that future models need to address.

"For making another 10x jump, of course, and other issues that we previously knew that they do exist... for the next one, we have to do it."

• Future advancements will require addressing known issues and making strategic system improvements.

• Initial phases of new hardware generation are fraught with high failure rates due to unanticipated issues.
• As understanding of infrastructure improves, failure rates decrease, enhancing uptime and availability.
• Planning for steady-state operations may lead to poor availability during early phases due to unpredictable failure risks.

"There are issues that are early on in the lifetime of a new generation of hardware...the failure rates are quite significant earlier in the run."

• Early phases are marked by significant failure rates due to unanticipated issues in new hardware generations.

"Later on, the failure rates drop significantly, the uptime improves overall, and so on."

• As infrastructure understanding improves, failure rates decrease, enhancing overall uptime.

• Exploration of the potential of classical pre-trained models with unlimited resources but existing limitations.
• Discussion on the potential to reach GPT 5.5 with current knowledge and constraints.
• Shift from compute-constrained to data-bound research environment, leading to new research excitement.

"If we were going to put this aside for a second and think about just how far we could go on classical pre-trained models...how far could we go like GPT what could we train with what we know today?"

• Exploration of potential advancements in classical pre-trained models with current knowledge and constraints.

"We're no longer compute constrained on the best models we can produce."

• Shift from a compute-constrained to a data-bound research environment, leading to new research opportunities.

• Unexpected scaling behaviors of different machine learning components during GPT 4.5 training.
• Discovery of nuanced abilities in the model not anticipated beforehand.
• Enhanced intelligence and common sense knowledge observed in user interactions.

"One of the more surprising things that we found...was just kind of how different aspects of what we were working on...scaled."

• Unexpected scaling behaviors of machine learning components during GPT 4.5 training.

"The model has all of these incredibly nuanced abilities that were not in anyone's bingo card."

• Discovery of nuanced abilities in the model not anticipated beforehand.

• Significant impacts of mid-training changes leading to improved performance.
• Team's morale and motivation boosted by resolving key issues and achieving performance boosts.
• Persistent collaboration and teamwork beyond initial project launch.

"Some of the changes that we made during the run had a quite good impact...better than anticipated."

• Mid-training changes leading to significant performance improvements.

"Seeing the moment that the whole team once a few of those issues got resolved...everybody feels excited and now more motivated."

• Team's morale and motivation boosted by resolving key issues and achieving performance boosts.

• Extensive planning and de-risking efforts undertaken a year before training began.
• Careful sequencing and scaling studies to ensure persistent feature benefits.
• Iterative improvement of scaling laws methodology guiding future projects.

"We started basically working on this project like a year before we even started training the run itself."

• Extensive planning and de-risking efforts undertaken a year before training began.

"We continue to iterate on our scaling laws methodology...guiding us for future GPGs."

• Iterative improvement of scaling laws methodology guiding future projects.

• Expectation of bugs during launch and the necessity of forward progress despite them.
• Systems built for visibility and distinguishing between hardware and software faults.
• Surprising discovery of a rare upstream PyTorch bug affecting the run.

"It is very unlikely that we launch a run and it doesn't have bugs."

• Expectation of bugs during launch and the necessity of forward progress despite them.

"The one that turned out to be the bug got the least votes...a simple summation and upstream PyTorch."

• Surprising discovery of a rare upstream PyTorch bug affecting the run.

• The process of identifying and fixing bugs in complex systems can reveal underlying issues affecting multiple areas.
• A single bug fix can resolve seemingly unrelated problems, highlighting the interconnected nature of software issues.
• Engineers must meticulously examine code paths, even those rarely used, to identify potential bugs.

"Once somebody fixed the bug, I mean, our engineers figured out, 'Oh, I found the bug. It is this line. Let's ship a fix and see if it fixes everything.' It fixed all the way of sanding bugs that where they had seemingly distinct symptoms."

• A single bug fix resolved multiple issues, indicating the complexity and interconnectedness of software problems.

"Somebody started looking through the code and different code paths and said, 'Oh, this very unlikely code path that probably most people don't hit, we do hit.'"

• Engineers identified a rarely used code path that was causing issues, emphasizing the importance of thorough code review.

• Continuous monitoring of machine learning runs is crucial to identify unexpected trends and make improvements.
• Engineers focus on both system and algorithmic improvements post-launch to enhance performance.
• There is a reliance on observing trends and interpreting noisy signals to assess the health of a machine learning run.

"There's a lot of things that we try to continuously monitor of the run to see if anything is kind of trending like we're not expecting."

• Continuous monitoring helps in identifying unexpected trends and making necessary adjustments.

"Imagine there's a lot of noisy signal, and you are at times reading tea leaves. It's just, is this healthy or not?"

• Engineers must interpret noisy signals to determine the health of a machine learning system.

• Improving data efficiency in machine learning is a key area of interest, with current algorithms being far from human-level efficiency.
• There is optimism about stacking algorithmic improvements to enhance data efficiency over time.
• The focus has traditionally been on compute efficiency, but data efficiency is becoming increasingly important.

"Humans, for whatever other flaws we have about learning things, we seem unbelievably data efficient."

• Human data efficiency is significantly higher than current machine learning algorithms, highlighting a gap in capabilities.

"For decades, deep learning has been about compute efficiency... We're entering a new stage of AI research where we'll be stacking data efficiency wins 10% here, 20% there."

• The focus is shifting towards data efficiency, with incremental improvements expected to accumulate over time.

• There is speculation about the feasibility and nature of future large-scale machine learning runs involving millions of GPUs.
• Such runs may not be fully synchronous due to technical limitations but could be semi-synchronous or decentralized.
• The potential for large-scale runs exists, but they may differ significantly from current methods.

"Will humanity ever do a 10 million GPU or greater synchronous pre-training run? I don't know if it'll exactly be a pre-training run, but I think there'll probably be some kind of training run that there will be 10 million GPU training."

• Large-scale GPU training runs are likely, but they may not resemble current pre-training runs.

"I would call it semi-synchronous, and the scale of it I hope so. I think it sounds very interesting."

• Future large-scale runs are expected to be semi-synchronous due to technical constraints.

• Larger, smarter pre-trained models tend to have better generalization capabilities, aiding in reasoning tasks.
• Pre-training offers broad-based intelligence improvements, while reasoning skills may be more domain-specific.
• The breadth of training data in pre-training contributes to its general applicability across tasks.

"Better pre-training and unsupervised learning just tends to lift kind of broad-based intelligence of the model and aid a lot in generalization."

• Pre-training enhances general intelligence and generalization, making it complementary to reasoning.

"Pre-training is essentially compressing the data and compressing the data is about seeing connections between different things."

• Pre-training involves data compression, which helps in recognizing patterns and connections across different domains.

• The adaptability of code design to infrastructure determines the bottlenecks in scaling AI systems.
• There is no single bottleneck; instead, systems can be adjusted to accommodate different limitations.
• Effective code design allows for flexibility in addressing potential bottlenecks like network, memory, or compute limitations.

"If you do code design, the workload is adaptable to the infrastructure that you built."

• Code design flexibility allows for adaptation to various system constraints, preventing a single bottleneck.

"There is no statement that broadly network is a bottleneck or memory bandwidth is a bottleneck or computer is a bottleneck."

• The absence of a single bottleneck highlights the importance of adaptable system design.

• The discussion begins with the importance of balancing system resources, highlighting the difference between pre-training and inference demands.
• Emphasis is placed on the need for more memory bandwidth to optimize model performance.
• Collaboration between teams is crucial for optimizing model specifications and system architecture.

"We have the option of shifting resource demands to basically create a more balanced system."

• Shifting resources can help create a more balanced and efficient system.

"Speaking of that earlier point, how much do your teams work together on like the spec of the model as we get ready for the four five run?"

• Collaboration between teams is essential for preparing model specifications and optimizing performance.

• The podcast discusses the importance of co-design in integrating machine learning (ML) and system architecture.
• The co-design effort involves a deep collaboration to ensure ML and systems work well at scale.
• The goal is to create a balanced and symmetrical system through co-design.

"For this project, it was a much deeper collaboration going back six or nine months before the launch of the run."

• Early and deep collaboration is vital for successful system and ML integration.

"The code design effort is something that formed the architecture and architectural elements that go into the model."

• Co-design is crucial for developing the architecture that integrates ML and systems.

• The discussion explores the gap between idealized system designs and practical constraints.
• The practice of building systems involves reconciling ideal designs with real-world limitations.
• The process is about approximating the ideal to the degree possible.

"We are nowhere near the idealized mean system, but it is fun."

• There is a significant gap between ideal system designs and current capabilities, but the process is engaging.

"It's just about reconciling the differences of that with what you have."

• Building systems involves reconciling ideal designs with practical limitations.

• Unsupervised learning is described as a process of compression, finding the shortest program to explain data.
• The concept of Solomon induction is introduced, which involves considering all possible universes with simpler ones being more likely.
• Pre-training is seen as a way to compress data and approximate intelligence.

"The ideal intelligence is called Solomon induction, basically uncertain about what universe it's in."

• Solomon induction involves considering all possible universes, prioritizing simpler ones.

"What we're doing with pre-training is compressing, trying to find the shortest program that explains all of the data."

• Pre-training involves compressing data to find the most efficient explanation.

• The choice of metrics is critical in evaluating machine learning models.
• Perplexity is discussed as a key metric for evaluating model performance.
• The importance of using held-out data that is not present in the training set is emphasized.

"The thing you get when you do these scaling laws and ML science is very dependent on the metric that you choose."

• The choice of metrics significantly impacts the evaluation of ML models.

"We care a lot about them not being present in any degree to the slightest degree in our training set."

• Ensuring held-out data is not present in the training set is crucial for accurate evaluation.

• Scaling laws are discussed as a property of the universe, similar to quantum mechanics.
• The discussion explores why training larger models for longer leads to more compression and intelligence.
• The concept of sparse relevant concepts in data and power laws is introduced.

"Scaling laws keep going and probably keep going for a long time."

• Scaling laws are persistent and fundamental to understanding model performance.

"The relevant concepts are sort of sparse in the data of the world, and it's a power law."

• Sparse relevant concepts and power laws explain why scaling models improves performance.