MemRole: From Memories to Decisions for Long-Horizon Reasoning in Role-Playing Agents
Read the original on arXiv Computation and Language →MemRole is a framework that explicitly models the memory-to-decision process for large language model (LLM) role‑playing agents, enabling them to make role‑consistent decisions over long narrative horizons. It introduces Long‑Horizon Memory Thinking (LHMT), a reasoning schema that decomposes hidden reasoning into context understanding, memory activation, and response strategy planning, and is trained on a novel dataset that limits each character’s memory pool to what it could plausibly know at the current narrative point. The framework also includes Self‑Refining On‑Policy Self‑Distillation (SR‑OPSD), where the model critiques its own rollouts and distills token‑level guidance back into the policy, resulting in a 15.2‑point improvement over its backbone and competitive performance with frontier LLMs such as GPT‑5.4.
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