3 accepted papers
Prompt optimization methods have demonstrated significant effectiveness in aligning black-box large language models (LLMs). In parallel, inference scaling strategies such as Best-of-N Sampling and Majority Voting have likewise been shown to improve alignment and performance by trading additional com
The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretability. To address these issues, we introduce MAPLE,…
Robotic systems are often composed of modular algorithms that each perform a specific function within a larger architecture, ranging from state estimation and task planning to trajectory optimization and object recognition. Existing work for specifying these systems as a formal composition of contra…