Grammar and Gameplay-aligned RL for Game Description Generation with LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913914616283136 |
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| author | Tanaka, Tsunehiko Simo-Serra, Edgar |
| author_facet | Tanaka, Tsunehiko Simo-Serra, Edgar |
| contents | 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. Our code is available at https://github.com/tsunehiko/rlgdg |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grammar and Gameplay-aligned RL for Game Description Generation with LLMs Tanaka, Tsunehiko Simo-Serra, Edgar Computation and Language Artificial Intelligence 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. Our code is available at https://github.com/tsunehiko/rlgdg |
| title | Grammar and Gameplay-aligned RL for Game Description Generation with LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15783 |