Portrait of Sophie Broderick

Sophie Broderick

Princeton University
sb4170@princeton.edu

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About Me

Princeton CS 2027 | Reinforcement Learning, LLMs, Semantic Representations, Diversity
Research @ Reinforcement Learning Lab

Let’s talk about research or ideas for making adaptive intelligent systems that align with humanity.

I also create art and write humor focused on the tech world, AI, and the tension between humans and machines.

Selected Research

  • NeurIPS 2026 LP4FM, WiML & Queer in AI poster | Escaping the Obvious: Reinforcement Learning for Conceptually Diverse Generation
    Sophie Broderick, Qiawen Liu, Benjamin Eysenbach
    Large Language Models often converge to a narrow set of high-probability ideas. This research investigates methods to mitigate this tendency, evaluating how RL changes the semantic geometry of model outputs for conceptually diverse generation.

  • Empowerment—an objective that measures how much influence an agent has over its environment—provides a principled way to learn concise representations that capture only the aspects of the environment that matter for control while ignoring irrelevant distractors.

  • This project evaluates the robustness of Maximum Entropy Reinforcement Learning (MaxEnt RL) via Energy-Based Normalizing Flows (EBFlow). While RL algorithms are typically tested in clean, idealized simulations, this work introduces realistic disturbances—including sensor corruption, action noise, and observation noise—using tools from the Robust Gym benchmark to assess generalization.


Published Humor


Art

Interactive Art



Animated interactive artwork with a distortion effect





Intimate Machines


Four Types of AI





Portraits