How To Train An LLM with Anthropic's Head of Pretraining

Y Combinator

426 views • 20 days ago

Video Summary

The discussion delves into the intricacies of AI pre-training, starting with its fundamental concept: predicting the next word in a sequence. This method, fueled by the vastness of the internet, has proven to be a powerful engine for creating increasingly intelligent AI models, as evidenced by the scaling laws that predict performance improvements with more compute, data, and parameters. The conversation highlights how the autoregressive approach to pre-training, as seen in models like GPT, has become dominant due to its empirical success and its direct applicability to generating text, enabling a virtuous cycle of model improvement and revenue generation.

The dialogue then navigates the practical challenges of large-scale AI development, including the complex infrastructure required, the optimization of hardware utilization, and the meticulous process of debugging at massive scale. It touches upon the evolution from early, less theoretical discussions of AI safety to the current focus on practical applications. The complexities of data availability, the rise of synthetic data, and the critical role of robust evaluation metrics are explored, underscoring that while "loss" is a primary metric, its effectiveness hinges on accurately reflecting desired outcomes without being susceptible to noise or gaming.

Finally, the conversation shifts to the broader implications of AI, particularly the concept of alignment. This involves not just ensuring models perform tasks as intended, but also shaping their "personality" and aligning their goals with human values, especially as AI capabilities surpass human intelligence. The discussion emphasizes that while pre-training builds intelligence, post-training methods are crucial for refining model behavior and personality, with an ongoing debate about the optimal balance and integration of these approaches for responsible AI development and deployment.

Short Highlights

Pre-training, primarily through next-word prediction, is a core method for advancing AI capabilities, driven by scaling laws that show predictable improvements with increased compute, data, and model size.
Developing large-scale AI models necessitates sophisticated infrastructure, efficient hardware utilization, and meticulous, often challenging, debugging processes.
The availability and quality of data are crucial, with discussions around the vastness of internet data, the use of synthetic data, and the need for effective, low-noise evaluation metrics beyond simple loss.
AI alignment is a critical concern, focusing on ensuring models share human goals and exhibit desirable behaviors, with post-training techniques playing a significant role in shaping model personality and values.
Future advancements in AI are expected to involve further scaling, potential paradigm shifts, and the ongoing challenge of solving complex, subtle bugs that can significantly derail development timelines.

Key Details

The Genesis of Pre-training: Scale and Next-Word Prediction [3:30]

The core idea of pre-training is to leverage scale, specifically by using massive datasets like the internet.
The objective is to predict the next word in a sequence, which provides a dense signal for learning.
This autoregressive approach has been empirically proven to lead to smarter models as more compute and data are applied.
Scaling laws describe how model performance (measured by lower loss or better next-word prediction) improves predictably with increased compute, data, and parameters.
This creates a positive feedback loop where better models can be used to generate revenue, which then funds more compute for even better models.

The fundamental thesis of pre-training centers on the power of scale, utilizing vast datasets from sources like the internet to train models through next-word prediction. This approach, supported by empirical evidence and scaling laws, has consistently led to more intelligent AI systems, creating a self-reinforcing cycle of development and improvement.

The internet is massive. It's probably the biggest like single source of data humanity has created. And you don't have labels. It's like you don't want someone to have to go in and look read the entire internet and like say something about it. So you want to get labels out of the data itself.

Empirical Dominance of Autoregressive Pre-training [5:10]

While various pre-training objectives were explored (e.g., BERT, BART's masked language modeling), autoregressive modeling (next-word prediction) emerged as dominant.
A key advantage of autoregressive models is their ability to generate text directly by sampling, which is highly conducive to product development and revenue generation.
Perfecting language modeling could theoretically enable a model to write entire papers from a title, demonstrating its generative power.

The empirical success of autoregressive modeling for pre-training, as opposed to other objectives like masked language modeling, has largely dictated its dominance. Its inherent ability to generate text smoothly has made it exceptionally well-suited for practical applications and the iterative cycle of development and commercialization.

I think the answer is like it's mostly empirical like in terms of how to think of the things I'd be like yeah it's empirical just try them all see what works.

The Challenge of Hyperparameter Optimization and Scaling Laws [7:15]

Training large models involves optimizing hundreds of hyperparameters (e.g., number of layers, width).
The primary challenge is balancing the effort of precise optimization against simply applying more compute.
Scaling laws indicate that while hyperparameter tuning can yield incremental gains, substantial increases in compute often lead to reliable improvements.
However, significant misconfigurations can halt progress, and the lack of counterfactual runs makes it hard to diagnose failures.
Scaling laws predict a power-law relationship between compute and loss, with deviations indicating potential issues.

The process of training massive AI models is fraught with the challenge of optimizing numerous hyperparameters. While significant compute is the primary driver of improvement, as evidenced by scaling laws, meticulously fine-tuning these parameters is crucial, yet difficult to perfect without extensive experimentation and sophisticated diagnostic tools.

You know, how many layers do you have? What's your width? Like you have the space of hundreds of hyperparameters and you want them all to be optimal and you're sort of striking this balance actually between how much do they matter like can you just take your best guess and throw more compute at it in whatever way you want and basically doesn't matter.

Infrastructure and Efficiency in Early AI Development [9:30]

In its early stages, even with significant funding, the team operated with a relatively small number of people, requiring efficient use of infrastructure.
Despite the perceived frontier of AI development, the number of individuals working on these core challenges was surprisingly small.
The cost of training models like GPT-3 ($5 million) was significant but manageable for a well-capitalized startup.
Cloud providers were utilized, but a deep understanding of hardware, including physical chip layout and network latency, was essential.
Developing highly efficient distributed training frameworks was crucial, particularly when competing with more established research labs.

During its nascent stages, the organization prioritized hyper-efficiency in infrastructure and resource utilization. This was driven by a smaller team and less abundant funding compared to larger entities, necessitating a deep, hands-on understanding of the underlying hardware and distributed systems to maximize computational impact.

Um like the public estimates for GP3 I remember were that it cost $5 million to train which you're on the one hand five million is kind of a lot but it's like a lot for an individual person. It's not really a lot from like a company perspective.

Building Distributed Systems and Customization [11:50]

Training large models requires distributing computation across numerous chips using techniques like data parallelism and pipeline parallelism.
At the time, robust open-source packages for these distributed training methods were scarce, necessitating in-house development.
The decision to build custom solutions, rather than relying on external packages, was driven by the need for scalability beyond existing frameworks and the desire for full control and modification capabilities.
This involved a counterintuitive commitment to building systems that could operate at a scale exceeding even well-established research labs.

The early development of AI infrastructure involved significant in-house engineering to create custom distributed training frameworks. This was a strategic necessity due to the lack of mature open-source solutions and the ambition to scale beyond the capabilities of existing tools, enabling the team to push the boundaries of computation.

And and I'm curious if you could speak to how you guys thought about that. like what did your infrastructure even look like to do that type of determination?

The Subtlety of Hardware and Software Interaction [14:32]

Training large models involves working at a level of abstraction below typical high-level libraries like PyTorch, often requiring deep understanding of operations like matrix multiplication.
For complex components like attention mechanisms, deeper optimization down the software stack, potentially including custom CUDA kernels, becomes necessary.
The process involves modeling efficiency, calculating theoretical utilization (MFU), and using profilers to identify and rectify performance bottlenecks.
This requires a deep understanding of hardware constraints, such as memory bandwidth and CPU offloading, and the ability to predict and match operational performance.

Implementing and optimizing large-scale AI models demands a sophisticated understanding of hardware and software interactions, often requiring work at a lower level than standard libraries. This involves detailed performance modeling, profiling, and a deep dive into hardware constraints to achieve maximum efficiency.

The reason you don't get good MFU is you end up limited on HBM bandwidth you end up limited on I don't know as host to like CPU offload there's a bunch of different pieces but there's not that many pieces there's like six relevant numbers there so you can totally model it out understand what the constraints are and then implement something that can get there

Learning and Debugging at Scale [16:55]

Learning complex systems, especially at scale, is significantly accelerated through pair programming with experienced individuals.
Debugging multi-node training jobs on thousands of GPUs presented significant challenges, often requiring deep dives into profilers and custom hacks to combine traces.
Traditional debugging methods, like using print statements, proved insufficient for complex, distributed systems, highlighting the necessity of effective debuggers.
Mastering the entire stack, from high-level AI concepts down to low-level hardware and networking, is a rare but crucial skill for tackling deep-seated bugs.

Mastering the intricate world of large-scale AI development involves a steep learning curve, heavily reliant on collaborative learning through pair programming and the development of specialized debugging skills. Tackling issues in distributed systems at this scale demands a comprehensive understanding of the entire technology stack, from AI models down to the underlying hardware and networking.

But you also learn how people do it. So something like a pro how to use a profiler is not something you would ever learn from seeing someone's like final write up on Slack for their PR. You would just be like, "Oh, they found these. They changed this specific line and it's a win."

Evolution of Pre-training Strategy and Team Specialization [18:41]

The core pre-training objective of reducing loss remains constant, but the strategy has evolved with increased specialization within teams.
Early on, individuals could track the entire codebase, but as teams grow, expertise becomes more focused on specific areas like attention mechanisms or parallelism strategies.
This specialization allows for deeper optimization but necessitates strong managerial oversight to maintain a cohesive vision and avoid single points of failure.
There's a trade-off between generalists who understand the big picture and specialists who can achieve deep expertise in narrow domains.

As AI development has scaled, pre-training strategies have adapted, leading to increased team specialization. While the fundamental goal of reducing model loss persists, the organizational structure has shifted from broad understanding to deep expertise in specific areas, requiring careful management to balance these approaches.

I think the biggest things that have changed has been a little more specialization. Like I think at the beginning, I mean the first like 3 or 6 months I tried to read every PR in the codebase and that was great. I knew all the pieces etc.

Challenges of Scale: Connectivity and Reliability [21:58]

Scaling up compute involves connecting an increasing number of GPUs, leading to challenges in maintaining robust connectivity.
Standard parallelization methods can create single points of failure, where the failure of one chip can bring down the entire system.
The novelty of hardware, from data center layouts to the chips themselves, means that hardware failures are a real and often unpredictable concern.
Debugging in this environment requires considering not just software bugs but also potential hardware malfunctions, which can be difficult to diagnose.

The exponential growth in AI compute brings significant infrastructure challenges, particularly concerning the reliability and connectivity of increasingly large clusters of GPUs. The sheer scale and novelty of the hardware introduce complexities where hardware failures can disrupt long training runs, demanding a robust approach to both software and hardware troubleshooting.

The standard way people paralyze chips isn't um the whole thing is one failure domain like one chip fails the whole thing can crash

Navigating Diverse Hardware and Collaborative Debugging [25:10]

Different compute architectures, such as TPUs and GPUs, have distinct characteristics in terms of compute power, memory, and bandwidth, requiring tailored programming approaches.
Workloads like inference benefit from high memory bandwidth, while pre-training is often more compute-intensive with larger batch sizes.
The diversity of hardware necessitates writing code multiple times or developing complex abstractions, potentially multiplying development effort.
Collaborating with hardware providers to fix bugs in new chips is a common practice, involving creating small-scale reproducible tests to isolate issues.

The AI landscape involves a diverse range of computing hardware, each with its unique strengths and programming requirements. Effectively leveraging these different architectures for specific workloads like pre-training and inference necessitates careful consideration of their specifications and often involves close collaboration with hardware providers to resolve emerging issues.

You know, some some might have like a lot of flops and not very much memory or they might have a lot of memory bandwidth but not very much memory. So I think a lot of having multiple chips is like great in some ways. It means you can actually like take the job and put it on the chip that it works best on.

The Interplay of Pre-training and Post-training in AI Development [28:14]

AI development has seen a shift towards a more balanced focus on both pre-training and post-training techniques, such as reinforcement learning (RL) and fine-tuning.
While pre-training establishes the foundational intelligence, RL and other post-training methods offer significant opportunities for improvement and customization.
The question of how to optimally balance compute allocation between pre-training and post-training is an ongoing area of research.
Empirical testing is paramount in determining the most effective combination of these approaches, as theoretical models often fall short in practice.

The current era of AI development sees a crucial interplay between pre-training and post-training methodologies. While pre-training lays the groundwork for intelligence, techniques like reinforcement learning are proving to be powerful drivers of further model advancement, necessitating a strategic approach to balancing resources between these two critical phases.

Yeah. So I think yeah there sort of used to be this idea of like I mean it's funny because the original name pre-training implies that like a small thing you're going to do this big training thing and that like and there was there was actually one shift already which was like no you just do a lot of pre-training like you use most of your computing

Data Availability and the Challenge of AI-Generated Content [31:00]

There are ongoing debates about the availability of high-quality data for pre-training, with some asserting that the internet's usable data has been largely exhausted.
The continuous growth of the internet and the increasing amount of compute available present a dynamic relationship between data supply and demand.
Defining the "useful" internet for AI training is challenging, as traditional metrics like PageRank may not fully capture the value of data for AI models.
The increasing prevalence of AI-generated content on the internet raises concerns about potential model collapse or the degradation of data quality if models are trained on their own outputs without careful curation.

The question of data availability for AI pre-training is complex, with prevailing opinions suggesting a potential scarcity of high-quality, human-generated text. The rise of AI-generated content adds another layer of complexity, raising concerns about data contamination and the potential for models to inadvertently learn from flawed or recursive information.

I think there's a funny thing where I feel like on data I see so many really confident takes on we're out of internet like this point scaling has ended and I'm almost a little bit like unsure exactly how much data people are using.

The Importance of Robust Evaluation Metrics [37:01]

While "loss" is a primary metric in pre-training, effective evaluation requires metrics that genuinely measure desired outcomes and are low-noise for decision-making.
The challenge lies in developing evaluations that accurately capture the capabilities we care about, as proxies can be misleading and easily saturated.
Metrics need to be fast and easy to run to facilitate rapid iteration and decision-making.
For AI doctors, for example, a crucial evaluation would involve assessing performance in long conversations with patients, not just answering exam questions, which is difficult to quantify.

Effective evaluation in AI development hinges on metrics that are not only accurate but also low-noise and readily applicable. While fundamental metrics like "loss" are important, the real challenge lies in designing evaluations that truly reflect desired complex behaviors, especially in specialized domains where human-like interaction is key.

Ultimately, like the qualities I like for an eval are like number one, is it actually measuring something you care about? Like you proxies can be pretty annoying cuz like we saturate evals pretty fast and there's sort of this pattern.

Defining and Implementing AI Alignment [41:06]

Alignment is about ensuring AI systems, particularly advanced ones nearing or exceeding human intelligence (AGI), share human goals and values.
Next-token prediction, a common pre-training objective, is not an inherent goal, necessitating alignment efforts.
Alignment can be approached theoretically or empirically by assessing whether models behave as desired.
It also involves controlling a model's "personality" or interaction style, distinct from its core intelligence, often addressed through techniques like constitutional AI.

AI alignment is a multifaceted endeavor focused on ensuring that artificial intelligence systems, especially those approaching or surpassing human cognitive abilities, operate in accordance with human intentions and values. This involves guiding their ultimate goals and shaping their interaction styles to be beneficial and safe.

I think an alignment is like how do you get the model to share the goals that you have particularly and I think it's particularly interesting once you get to like models that are smarter than you are

The Role of Pre-training vs. Post-training in Alignment [45:16]

Most alignment interventions are best performed in post-training due to the rapid iteration cycles and the need for models complex enough to exhibit meaningful behaviors.
Pre-training offers slower iteration and the challenge of testing complex interventions on models that are not yet sufficiently capable.
However, some alignment principles might eventually be integrated into pre-training to embed them more robustly into a model's intelligence.
This could involve incorporating human feedback characteristics into the pre-training data or process, though it risks sacrificing flexibility in model behavior adjustment.

While post-training methods offer a more agile approach to AI alignment due to faster iteration cycles, there is a potential for certain alignment principles to be integrated into the pre-training phase. This could enhance the robustness of these principles within the model's core intelligence, though it may reduce the flexibility to adjust model behavior later.

I think that's probably the the right way to think about it for the most part I think like I the way I usually think about it is anything you can do in post training you probably should because your iteration loop like the ability to make progress is really fast

Navigating Future AI Development: Paradigm Shifts and Hard Bugs [47:39]

Future AI development is anticipated to involve paradigm shifts beyond current approaches, potentially including new methods for RL and other advancements.
A significant concern is the emergence of hard-to-solve bugs in complex systems, where a single issue can derail months of progress due to the long training times of models.
Debugging ML systems is inherently difficult, and these challenges are amplified at scale, where subtle errors in code, hardware, or networking can manifest as catastrophic failures.
Solving these deep-seated bugs requires a rare skill set encompassing a comprehensive understanding of the entire technology stack, from high-level AI concepts to low-level hardware and network protocols.

Looking ahead, AI development is poised for significant paradigm shifts and the continued challenge of tackling complex, deep-seated bugs. The long training cycles of advanced models mean that even minor undetected errors can lead to substantial setbacks, underscoring the critical need for engineers with a holistic understanding of the entire AI system.

I think the things that feel most top of mind to me are probably like paradigm shifts like I think the sort of shift towards uh more RL is like one paradigm shift in the field and I I think it's I think there will probably be more.

The Crucial Role of Engineering in AI Advancement [52:18]

While AI research is often associated with PhD-level theoreticians, the advancement of AI heavily relies on skilled engineers.
The core architectures are relatively straightforward mathematically, but implementing them at scale, ensuring correctness, and debugging complex distributed systems are significant engineering challenges.
This engineering skill set involves not just coding but also the ability to debug deeply and understand the entire system stack.
There's a distinction between rapidly iterating on web applications and solving the complex, low-level engineering problems inherent in large-scale AI.

The rapid progress in artificial intelligence is profoundly driven by engineering expertise, which is essential for translating theoretical models into scalable, functional systems. This involves not only implementing complex architectures but also the critical ability to debug and troubleshoot intricate, distributed environments.

It's like the case that you throw more compute, the thing kind of works. Yeah. Uh the challenge is like actually the researchers are like cool, nice. Yeah. And getting it correct, like getting it correct isn't really an ML problem, right?

The Search for Specialized Talent and Startup Opportunities [53:34]

The field is increasingly hiring individuals with prior experience in large-scale AI development from other companies.
However, early on, there was success in hiring smart individuals from diverse backgrounds, such as theoretical physics, who could rapidly learn programming and contribute effectively.
Startups can find success by identifying specific problems within the AI development stack that larger organizations might overlook or lack the bandwidth to address.
Opportunities exist in areas like ensuring hardware reliability, optimizing specific software components, or providing consulting services for scaling challenges.

The recruitment landscape for AI development is evolving, with a growing demand for experienced engineers. While direct experience is valuable, the ability to learn quickly and adapt remains key, and startups can carve out niches by solving specific, often intricate, problems within the broader AI ecosystem.

Yeah. So at this point like I think we actually just hire a bunch of people who have done this before from like other places and that's like the easy answer.

Future Directions: Beyond Next-Token Prediction and Startup Niches [54:48]

While autoregressive modeling is likely sufficient to reach AGI, novel architectures and training methods are still being explored.
Architectural tweaks, such as improved caching and more efficient attention functions, are valuable but may not represent fundamental paradigm shifts on their own.
The primary drivers of progress are still scale and meticulous foundational science, though innovative changes can enhance efficiency.
Startups can thrive by focusing on areas that leverage current models to solve specific problems, especially those requiring significant implementation effort that larger labs might deprioritize.

The future of AI development involves a continuous exploration of both scaling current paradigms and investigating novel architectures. While autoregressive methods are likely to take us far, the efficiency gains from clever architectural changes and the strategic focus of startups on specific implementation challenges will be crucial in shaping the field's trajectory.

I think they're interesting. I think I like am less like ah autogressive is the way to go. On the other hand, I think auto reagive is probably good enough to get to AGI or something or not like yeah uh such that yeah I I see the main driver as scale and careful science of like sort of the basics more than like come up with something totally novel.

The Symbiotic Relationship Between Pre-training and Inference Teams [56:52]

The pre-training team significantly influences the challenges faced by the inference team by the models they produce.
Decisions made during pre-training, such as model size or training duration, directly impact the efficiency and feasibility of inference.
Making models too large or training them for too few tokens can create significant challenges for inference teams.
Close collaboration between pre-training and inference teams is essential for co-designing models that are both intelligent and cost-effective to deploy.

The pre-training process profoundly impacts the subsequent inference stage, as decisions made during training directly affect the efficiency and feasibility of deploying models. This necessitates a close, collaborative relationship between pre-training and inference teams to ensure that models are not only intelligent but also practical and economical to run.

Oh no. I think a ton about inference because basically like the problem inference is solving like we basically determine the problem inference is solving. We give them a model and they have to like run that fast and it's very easy to give them a model that is impossible to run fast.

The Indispensability of Compute and the Potential of Infinite Compute [57:54]

Compute is a significant bottleneck in current AI research and development, limiting the pace of experimentation and model iteration.
If compute were unlimited, the challenge would shift to effectively utilizing it, potentially exacerbating issues like chip failures and the need for advanced engineering solutions.
The current pace of AI progress is heavily dependent on the availability of compute; increased compute would allow for daily model retraining and rapid iteration.
The field is constantly advancing, with annual increases in available compute leading to significant leaps in AI capabilities.

The current state of AI research is intrinsically tied to the availability of computational resources, which act as a primary limiting factor. While increased compute would unlock new possibilities, the challenge would then shift to efficiently managing and utilizing these vast resources, underscoring the continuous need for sophisticated engineering and problem-solving.

Like the models that everyone uses, right? If you're using like Cloud Sonic 4, Cloud Opus 4, it's like it's our first shot at models at that scale, right?

The Future of AI and Responsible Development [1:00:10]

The future of AI holds immense potential for economic growth and societal transformation, with automation extending to nearly all human endeavors.
Startups can find success by addressing niche problems or building upon existing AI capabilities to create specialized applications.
However, there's a risk in investing heavily in solutions that might become obsolete with the next generation of AI advancements.
Long-term success may involve considering how AI can be guided towards beneficial societal outcomes, rather than solely focusing on technological advancement and economic gains.

The trajectory of AI development points towards unprecedented societal and economic transformation, with automation poised to reshape countless industries. While startups can capitalize on emerging opportunities, the long-term vision must also encompass responsible development, ensuring that these powerful technologies are guided towards beneficial outcomes for humanity.

I mean, the thing I'd maybe just push startups on is thinking a little bit about like uh this is maybe less technical, but just like what happens once we get AGI and like how to make sure that like goes well for the world or something.

Advice for Aspiring AI Professionals [1:03:04]

For those entering the field, focusing on AI, particularly engineering skills, is paramount, even if theoretical understanding seems more obvious.
The ability to implement, scale, and debug AI systems is highly valued.
Aspiring professionals should also consider the societal implications of AGI and how to contribute to its responsible development.
The landscape is rapidly evolving, and continuous learning and adaptation are essential for career growth in AI.

For individuals aspiring to build a career in AI, a strong emphasis on engineering skills, alongside an understanding of the broader societal implications of AI, is crucial. The field demands practical implementation capabilities, a knack for problem-solving at scale, and a forward-looking perspective on responsible AI development.

But I think certainly if I went back 10 years ago I would be like focus on AI. It's like the most important thing and particularly focus on engineering which I think felt very wouldn't have seemed obvious to me at the time that like the important thing was these engineering skills and not the like math and theoretical understanding of like you know uh SPMs and like all the kind of standard ML literature.