Previously, I was the founding engineer at Sourceress, where I built the infrastructure for our machine learning pipeline and human-in-the-loop labeling system. My background is in software engineering and productionizing machine learning. Building upon OpenAI’s recent work on scaling laws, my project explores how much pre-training on English helps when transferring across different languages as we vary model size and dataset size. I found that a) pre-trained English models help most when learning German, then Spanish, and finally Chinese and b) transfer from English to Chinese, German, and Spanish scales predictably in terms of parameters, data, and compute.

I have a background in Software Development, AI Fairness, and VR Game Development. I was interested in the Scholars program as a way of strengthening my research skills, learning from other talented people in the field, and moving into industry research or engineering positions. My project is exploratory, investigating prior work on opinion modeling from the context of deep learning. As these models generate more and more text, it’s important to understand the impacts they’ll have on the ecosystem of opinions and future models. In addition, I investigated what happens when models are iteratively trained on outputs from previous models.

My background is in physics, with a focus on dark energy, dark matter, and the large-scale structure of the Universe. For my project, I pre-trained a language representation model using a purely contrastive objective. I am interested in the generalizability and scalability of such models compared to models pre-trained with more traditional language modeling objectives. I am also curious about what factors influence the performance of contrastive language encoders. In this talk, I present our methodology and some preliminary results.

I joined the Scholars Program to build computer systems that better understand what people really value. I live in Washington, D.C. and lately, I’ve really enjoyed building fantastic contraptions with K’nex. My recent work at OpenAI has demonstrated that reward models trained on human feedback can support Reinforcement Learning. My project demonstrates that reward models can be trained on large-scale structured feedback extracted from websites.

I am a software engineer with an applied physics and aerospace background. My presentation explores the generalizability of models leveraging test time compute in a number of domains including autoregressive transformers, deep equilibrium models, and graph neural networks. In it, I ask: Given the constraints of limited training compute budget, can small adaptive models instead leverage test time compute to overcome the handicap of having a smaller number of learnable parameters? Lastly, we present mechanisms that show promise in reducing the computational cost and improving the performance of graph neural networks.

I was formally trained as a data scientist and architect, but I pivoted my career because AI has a much higher agency on our environment than conventional industries, and there are many interesting research problems in this field. In my project, I extended the well-known card game “SET” to investigate the relationship between vector representation dimension and task composition. I found non-contrastive models of X parameters to solve games that contrastive models of 2X+ parameters cannot. What can a contrastive model learn with vector representations of size 16/32/64/128/256/512? And what not?

I was drawn to the Scholar’s program because I’d seen some of what OpenAI’s models could do and I wanted to understand what it took to build and iterate such powerful models. Having the dedicated time to explore deep learning with great mentorship has been transformative in my ability to understand and contribute to the field! When I’m not working, I’m usually tinkering with gadgets or out seeking adrenaline with friends. My project explores the tradeoffs in using these other tokenization schemes and how these different tokenizations scale. I also consider an approach to learning a sequence’s segmentation instead of using a predefined one.

I almost majored in French in college because I’ve always loved language. I frequently watch movies and tv shows in other languages (yes - kdramas are at the top of that list) but I never imagined that my love of language would translate into me doing research in NLP. In my research, I explore the tradeoffs between model performance and the cost of training, and study scaling laws on different transformer architectures to understand the impact of transformer architecture on model performance.

I applied to OpenAI because I wanted the profound privilege to wrestle with questions that shape ever-complex AI systems. As a Cameroonian native who grew up in the US, I navigate multiple perspectives (scholastically, culturally and linguistically) and was curious to learn how AI learns from human commonalities and differences. The arduous rewards and constraint engineering process can sometimes lead to misalignment between a designer’s idea of success and its analytic specification. Furthermore, many real-world tasks contain multiple objectives and current approaches in reinforcement learning do not offer a direct lever to choose between Pareto-equivalent strategies. To address these problems, in my project, I explain how we use “multiple experts, multiple objectives” (MEMO) to explore an agent’s ability to consume examples of success from multiple experts with different objectives, and learn a single conditional policy that can be oriented at the discretion of a supervisor.