Vikhrmodels/physics-generalization

该数据集包含1,000,020个多样化的2D刚体物理模拟场景，用于训练和评估LLM在物理预测任务上的表现。数据集涵盖了30种不同的场景类型，分为6个类别，包括碰撞、堆叠、斜坡、摆锤、约束和受迷你游戏启发的物理现象。每个场景是一个200帧的模拟，使用Pymunk（Chipmunk2D）物理引擎生成，并以JSONL格式导出，包含丰富的元数据。数据集分为训练集和验证集，训练集包含900,000个场景，覆盖24种场景类型；验证集包含100,020个场景，包括训练集中未见的6种场景类型，用于评估模型的泛化能力。数据集的特点包括场景类型的多样性、难度分级、确定性生成、丰富的文本描述等。

This dataset contains 1,000,020 diverse 2D rigid body physics simulation scenes for training and evaluating LLMs on physics prediction tasks. It covers 30 distinct scenario types organized into 6 categories, including collisions, stacking, ramps, pendulums, constraints, and mini-game-inspired physics. Each scene is a 200-frame simulation generated using the Pymunk (Chipmunk2D) physics engine, exported in JSONL format with rich metadata. The dataset is split into a training set with 900,000 scenes covering 24 scenario types and a validation set with 100,020 scenes, including 6 unseen scenario types for evaluating model generalization. Key features include scenario diversity, difficulty scaling, deterministic generation, and rich text descriptions.