tilmanr

Overview * Solving the problem of mesa-optimization would probably be easier if we understood how models do search internally * We are training GPT-type models on the toy task of solving mazes and studying them in both a mechanistic interpretability and behavioral context. * This post lays out our model training setup, hypotheses we have, and the experiments we are performing and plan to perform. Experimental results will be forthcoming in our next post. * We invite members of the LW community to challenge our hypotheses and the potential relevance of this line of work. We will follow up soon with some early results[1]. Our main source code is open source, and we are open to collaborations. Introduction Some threat models of misalignment presuppose the existence of an agent which has learned to perform a search over actions to effectively achieve goals. Such a search process might involve exploring different sequences of actions in parallel and evaluating the best sequence of actions to achieve some goal.[2] To deepen our understanding of what it looks like when models are actually performing search, we chose to train simple GPT-2 like models to find the shortest paths through mazes. Maze-solving models provide a tractable and interesting object of study, as the structure of both the problem and solutions is extensively studied. This relative simplicity makes identifying and understanding search through the lens of mechanistic and behavioral experiments much more concrete than working with pre-trained LLMs and more feasible in the context of limited computational resources. Connections to mesa-optimization Mesa-optimizers are learned optimizers for an objective that can be distinct from the base-objective. Inner misalignment can occur when the AI system develops an internal optimization process that inadvertently leads to the pursuit of an unintended goal. Models capable of perfoming search are relevant for understanding mesa-optimization as search requires