Fake Player: Simulating Player Behavior to Distill Dialogues for LLM-based NPC Training

In the era of large language models (LLMs), games increasingly deploy LLM-based role-playing NPCs to replace traditional scripted NPCs, enabling more intelligent and dynamic interactions. To ensure persona consistency and output stability, these NPCs require fine-tuning for alignment, utilizing training data with dual dimensions: persona-aligned assistant responses and diverse, authentic user inputs reflecting real player behaviors. However, existing research prioritizes persona consistency in NPC responses while neglecting the diversity and authenticity of user-side inputs. This critical gap leads to NPC responses that are misaligned with genuine player interactions, significantly impairing player immersion and experience. Human annotation struggles to address this gap due to its inability to comprehensively cover the vast spectrum of player behaviors. Moreover, practical deployment constraints strongly favor small-parameter LLMs for NPCs, making data quality paramount. To bridge this gap, we propose: 1) Fake Player: A multi-agent LLM distillation framework where collaborative agents simulate expression-constrained human players to distill diverse, human-aligned dialogue data from large LLMs; 2) Distill Bench: A standardized benchmark for quantitatively assessing distilled data quality, bypassing costly NPC retraining. Extensive experiments on upstream data quality and downstream NPC performance demonstrate that Fake Player produces higher-quality dialogue data and yields greater improvements in small-parameter NPC models than existing data synthesis methods. The consistent ranking across the two evaluation stages supports using Distill Bench to guide data selection and refinement before fine-tuning, offering a practical way to reduce costly trial-and-error training.