The field of artificial intelligence is witnessing significant developments in code generation and game playing with world models. Researchers are exploring the potential of large language models (LLMs) to improve code understanding and generation beyond what can be learned from static code alone. This is achieved by mid-training LLMs on observation-action trajectories from various environments and performing multi-task reasoning reinforcement learning in verifiable coding and software engineering environments. Another area of focus is the application of LLMs to classical board and card games, where they are used to translate natural language rules and game trajectories into formal, executable world models. These models enable high-performance planning algorithms like Monte Carlo tree search (MCTS) to generate strategic and verifiable moves. Additionally, researchers are investigating the use of learned models in imperfect information games, which presents substantial challenges due to nuanced look-ahead reasoning techniques. Noteworthy papers in this area include: CWM: An Open-Weights LLM for Research on Code Generation with World Models, which introduces a 32-billion-parameter open-weights LLM for advancing research on code generation with world models. Code World Models for General Game Playing, which proposes using LLMs to translate natural language rules and game trajectories into formal, executable world models for high-performance planning algorithms. Look-ahead Reasoning with a Learned Model in Imperfect Information Games, which introduces an algorithm that learns an abstracted model of an imperfect information game directly from the agent-environment interaction for look-ahead reasoning.