Grammar and Gameplay-aligned RL for Game Description Generation with LLMs

• URL: http://arxiv.org/abs/2503.15783v1
• Date: Thu, 20 Mar 2025 01:47:33 GMT
• Title: Grammar and Gameplay-aligned RL for Game Description Generation with LLMs
• Authors: Tsunehiko Tanaka, Edgar Simo-Serra,
• Abstract summary: Game Description Generation (GDG) is the task of generating a game description written in a Game Description Language (GDL) from natural language text.<n>We propose reinforcement learning-based fine-tuning of Large Language Models (LLMs) for GDG (RLGDG)<n>Our training method simultaneously improves grammatical correctness and fidelity to game concepts by introducing both grammar rewards and concept rewards.
• Score: 12.329521804287259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Game Description Generation (GDG) is the task of generating a game description written in a Game Description Language (GDL) from natural language text. Previous studies have explored generation methods leveraging the contextual understanding capabilities of Large Language Models (LLMs); however, accurately reproducing the game features of the game descriptions remains a challenge. In this paper, we propose reinforcement learning-based fine-tuning of LLMs for GDG (RLGDG). Our training method simultaneously improves grammatical correctness and fidelity to game concepts by introducing both grammar rewards and concept rewards. Furthermore, we adopt a two-stage training strategy where Reinforcement Learning (RL) is applied following Supervised Fine-Tuning (SFT). Experimental results demonstrate that our proposed method significantly outperforms baseline methods using SFT alone.

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