# Sebastian Borgeaud

Sebastian Borgeaud is a computer scientist at Google DeepMind, co-author of the influential Chinchilla paper on scaling laws for large language models, and a contributor to foundational AI research.

Sebastian Borgeaud is a research scientist at [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), known for his work on scaling laws, model architecture, and training efficiency in large language models. He is a co-author of the 2022 paper "Training Compute-Optimal Large Language Models," which introduced the Chinchilla model and reshaped how the AI community approaches model size and training data allocation. His research has influenced the design of subsequent large-scale AI systems, including those at [OpenAI](https://www.wikiprompt.org/wiki/openai) and [Anthropic](https://www.wikiprompt.org/wiki/anthropic).

Borgeaud's work sits at the intersection of [machine learning](https://www.wikiprompt.org/wiki/machine-learning) theory and practical engineering, with a focus on understanding the empirical behavior of [neural networks](https://www.wikiprompt.org/wiki/neural-network) under different computational budgets. He has contributed to multiple projects at DeepMind, ranging from language modeling to multimodal systems, and his findings are widely cited in the field of [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence).

## Scaling Laws and the Chinchilla Paper

The Chinchilla paper, published in March 2022, established a new paradigm for training [large language models](https://www.wikiprompt.org/wiki/large-language-model). Borgeaud and his colleagues, including [Jordan Hoffmann](https://www.wikiprompt.org/wiki/jordan-hoffmann) and [Llion Jones](https://www.wikiprompt.org/wiki/llion-jones), demonstrated that for a given compute budget, the optimal model size and number of training tokens scale roughly equally, rather than the model size growing faster than data. This contradicted earlier assumptions from the 2020 [Kaplan](https://www.wikiprompt.org/wiki/david-kaplan) scaling laws, which suggested that model size should increase more rapidly than data.

Specifically, the team found that a 70-billion-parameter model trained on 1.4 trillion tokens outperformed much larger models like Gopher (280B parameters) and GPT-3 (175B parameters) on a range of benchmarks. The paper's practical recommendation - that most existing models were significantly undertrained - led to a shift in training practices across the industry, with companies like [OpenAI](https://www.wikiprompt.org/wiki/openai) and [DeepMind](https://www.wikiprompt.org/wiki/google-deepmind) increasing their data-to-parameter ratios in subsequent releases.

## Contributions to Model Architecture and Training

Beyond scaling laws, Borgeaud has worked on improving the efficiency and stability of training deep networks. He has contributed to research on [multi-head attention](https://www.wikiprompt.org/wiki/multi-head-attention) variants, [positional encodings](https://www.wikiprompt.org/wiki/positional-encoding), and [layer normalization](https://www.wikiprompt.org/wiki/layer-normalization) techniques that are now standard in transformer-based models. His work often involves large-scale distributed training, where he has helped develop methods to reduce memory usage and communication overhead.

One notable area of his research is the use of retrieval-augmented generation, where models access external knowledge during inference. Borgeaud co-authored the 2021 paper "Improving Language Models by Retrieving from Trillions of Tokens," which introduced the RETRO (Retrieval-Enhanced Transformer) model. RETRO demonstrated that a 7.5-billion-parameter model with retrieval could match the performance of a 70-billion-parameter model without retrieval on certain tasks, highlighting the potential of combining parametric and non-parametric memory.

## Impact on the AI Community

Borgeaud's findings have had a measurable impact on the broader [machine learning](https://www.wikiprompt.org/wiki/machine-learning) community. The Chinchilla paper's scaling laws are now a standard reference in academic papers and industry reports, and they have influenced the design of models such as [OpenAI](https://www.wikiprompt.org/wiki/openai)'s GPT-4 and [Anthropic](https://www.wikiprompt.org/wiki/anthropic)'s Claude. His emphasis on compute-optimal training has also sparked discussions about the environmental and economic costs of AI, as researchers seek to achieve better performance with fewer resources.

In addition to his research, Borgeaud has been involved in open-sourcing tools and datasets. He has contributed to DeepMind's open-source libraries and has spoken at major conferences, including NeurIPS and ICML, where he has shared insights on scaling and evaluation. His work is often cited in the context of [generative AI](https://www.wikiprompt.org/wiki/generative-ai) and the development of more efficient training regimes.

## Current Work and Future Directions

As of 2024, Borgeaud continues to work at [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), focusing on next-generation model architectures and training methods. His recent projects explore ways to reduce the computational cost of inference, such as [model pruning](https://www.wikiprompt.org/wiki/model-pruning) and quantization, as well as improving the alignment of models with human values through techniques like [RLHF](https://www.wikiprompt.org/wiki/rlaif) (Reinforcement Learning from Human Feedback).

He is also interested in the intersection of [deep learning](https://www.wikiprompt.org/wiki/deep-learning) and neuroscience, drawing inspiration from biological systems to design more efficient learning algorithms. While specific details of his ongoing projects are not public, his track record suggests that his future contributions will continue to shape the trajectory of [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) research.

## Selected Publications

- "Training Compute-Optimal Large Language Models" (2022, with Jordan Hoffmann, Llion Jones, et al.)
- "Improving Language Models by Retrieving from Trillions of Tokens" (2021, with Jack Rae, et al.)
- "An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models" (2023, with collaborators)

These papers are among the most cited in the field, and their methodologies are widely adopted in both academic and industrial settings.

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Source: https://www.wikiprompt.org/wiki/sebastian-borgeaud
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-09T01:58:08.85149+00:00