Daydreamer: Training and Evaluating Associative Memory Navigation for Persona-Grounded Generation

Creative writing often draws on personal experience through association: one memory cues another, and fragments of experience are selectively recombined into new material. Yet existing benchmarks largely evaluate either the final creative artifact or one-shot memory retrieval, leaving this associative process itself unmeasured. We introduce DAYDREAMBENCH, a benchmark for studying episodic memory navigation in persona-grounded creative writing. It contains 46,959 episodic memories across 201 persona–corpus identities and 343 held-out writing tasks, with executable reference flows that connect literary works to plausible experiential memories. We further propose DAYDREAMER, a Writer–Walker framework in which a learned Walker sequentially searches, follows associations, retains useful episodes, and curates an ordered memory context before generation. The Walker is trained on executable trajectories followed by online GRPO with rewards for writing utility, reference recovery, and navigation progress. Across two Writer backbones, Daydreamer produces substantially broader memory discovery and better associative-flow alignment than untrained and retrieval-based baselines, while achieving the highest overall writing scores. The gains are most pronounced in persona grounding and creative transformation, suggesting that effective memory-augmented creative generation depends not only on retrieving relevant experiences, but on learning how to navigate and organize them.