AntimLabs/combined-game-sft

--- license: mit task_categories: - reinforcement-learning - text-generation tags: - game-ai - sft - multi-task --- # Combined Game SFT Dataset Multi-game SFT dataset for training game-playing language models. ## Dataset Details - **Total Examples**: 7,188 - **Games**: 9 different environments - **Format**: Standard SFT with `prompt`, `completion`, and `source` columns - **Balanced**: Each game capped at 1,200 examples max ## Game Distribution | Game | Examples | % | |------|----------|---| | taxi | 1,200 | 16.7% | | snake | 1,200 | 16.7% | | cliff-walking | 1,000 | 13.9% | | frozen-lake | 1,000 | 13.9% | | flappybird | 710 | 9.9% | | minigrid-lockedroom | 620 | 8.6% | | pacman | 500 | 7.0% | | blackjack | 480 | 6.7% | | minigrid-lavagap | 478 | 6.6% | ## Source Datasets - AntimLabs/FlappyBird-SFT - AntimLabs/cliff-walking-sft - AntimLabs/taxi-sft-new - AntimLabs/MiniGrid-LockedRoom-SFT - AntimLabs/blackjack-sft - AntimLabs/Frozen-Lake-SFT - AntimLabs/Minigrid_LavaGap - AntimLabs/Pacman-Array-SFT - gokul8967/snake-sft-8x8 ## Usage ```python from datasets import load_dataset dataset = load_dataset("AntimLabs/combined-game-sft", split="train") # Filter by game pacman_data = dataset.filter(lambda x: x["source"] == "pacman") ```