Core Viewpoint - Classic nostalgic games like Sokoban and Tetris have become benchmarks for evaluating large models, with the o3-pro model recently surpassing previous performance limits in these games [1][2][6]. Group 1: Benchmark Performance - The o3-pro model successfully completed all levels of Sokoban, which previously had a benchmark limit at the sixth level [3][8]. - In comparison to the previous state-of-the-art model (SOTA), o3, the performance of o3-pro has doubled [3][10]. - The scoring system for Tetris involves calculating the number of placed blocks and the number of cleared lines multiplied by ten, until the game ends [13][22]. Group 2: Game Characteristics and Evaluation - The Lmgame benchmark includes several games, such as 2048, Candy Crush, Super Mario Bros, and Phoenix Wright, each with unique evaluation criteria [18][24]. - The evaluation for 2048 is based on the total value of merged blocks, while Candy Crush measures the total candies eliminated in a fixed number of rounds [24]. - The evaluation methods do not consider time as a factor, focusing instead on game-specific performance metrics [22][24]. Group 3: Model Development and Support - The project is developed by the Hao AI Lab at UCSD, which is affiliated with the machine learning systems and NLP labs [28]. - The lab has received funding from Google and NVIDIA, with NVIDIA donating a DGX B200 system to support their research [34]. - The benchmark is open-source, allowing interested parties to download and test their models [23].

Core Viewpoint - Classic nostalgic games like "Sokoban" and "Tetris" have become benchmarks for evaluating large models, with the o3-pro model achieving significant breakthroughs in these games [1][6]. Group 1: Benchmark Performance - The o3-pro model surpassed previous benchmarks by completing all levels of Sokoban, while the best prior model, o3, only reached the sixth level [2][3]. - In Tetris, the scoring system combines the number of placed blocks with ten times the number of cleared lines, and o3-pro's performance doubled that of o3 [3][13]. - The o3-pro model's performance is notable for its time-consuming operations, taking several minutes for each move [17]. Group 2: Game Evaluation Standards - The Lmgame benchmark includes various games, with specific evaluation metrics for each, such as total distance moved in Super Mario Bros and total candy cleared in Candy Crush [6][24]. - The evaluation does not consider time as a factor, focusing instead on game-specific performance metrics [22]. - The benchmark is open-source, allowing others to download and test their models [23]. Group 3: Development and Support - The project is developed by the Hao AI Lab at UCSD, which has received support from Google and NVIDIA [28][34]. - The lab has created multiple open-source projects, with FastVideo being the most starred on GitHub [32].