Internalized Experience: How Training Data Shapes Memory Formation in LLMs

Parametric memory enables large language models (LLMs) to retain information from past experience and reuse it in subsequent tasks. Yet how training data supports the internalization of reusable task rules remains unclear. We introduce FSMem (Finite-State Machine Memory), a state-transition framework for constructing training data and studying how data content and organization shape memory formation. We evaluate rule reuse in synthetic worlds, classical planning, and code execution through two tasks: identifying valid actions and predicting execution outcomes. Our experiments reveal several systematic learning patterns. Under matched training conditions, even unchanged task rules become harder to internalize when learned alongside a larger variety of action types; by contrast, scaling the training data to provide broader state–action coverage generally improves rule learning. We further find that training at a single execution depth produces depth-specialized behavior, with models performing best when training and test executions have similar lengths. Motivated by this finding, we construct cumulative corpora that combine short and long trajectories, which can outperform long-trajectory-only training while using fewer training tokens. Together, these results demonstrate that the same task rules can be internalized differently depending on how execution experience is constructed, and that understanding these learning patterns can guide the design of more effective training data for parametric memory.