On prompt optimization and coding agents

2026 • Powerpoint • PDF

Abstract. Language models (LMs) encode vast amounts of human knowledge in a queryable form and are now widely deployed across domains that previously could not directly benefit from language. Yet major challenges remain: how to effectively extract information (e.g., where and how to prompt the model), and how to ensure alignment with human values and preferences (e.g., personalization, safety, code quality). A path forward to solving these is to measure and quantify the objective, perform (test-time) optimization over the prompt space to improve it, and ultimately integrate the improvement back into the base model. This talk explores parts of this in two domains: 1) Prompt optimization and adversarial attacks---in [AdvPrompter](https://arxiv.org/abs/2404.16873) (ICML 2025) we learn a model to rapidly predict the optimal adversarial suffix for jailbreaking an LM, in [AdvPrefix](https://arxiv.org/abs/2412.10321) (NeurIPS 2025), we show variants on the jailbreaking optimization problem objective can result in significantly improved performance, and in [AdvGame](https://arxiv.org/abs/2512.20806) we build an alignment method on these improvements; and 2) Numerical code synthesis with agents---in [AlgoTune](https://arxiv.org/abs/2507.15887) (NeurIPS D&B 2025), we propose a benchmark suite of 154 numerical coding tasks along with a baseline code agent to solve it. The coding agent iteratively optimizes the code (and prompts) for the task's runtime as the objective. We'll draw connections between these two seemingly separate settings and show how meta-learning and agentic test-time computations squeeze the most out of the base model, advancing both capabilities and alignment.