The field of artificial intelligence is witnessing significant developments in reinforcement learning and vision-language models. Researchers are exploring innovative approaches to improve the performance of large language models in complex environments. One notable direction is the use of self-play and reinforcement learning to enhance strategic reasoning and decision-making in multi-agent systems. Another area of focus is the development of more effective vision encoders for multimodal language models, which has led to improved performance in vision-language benchmarks. Furthermore, there is a growing interest in leveraging self-supervised learning and imitation learning to create more efficient and powerful vision-language models. These advancements have the potential to revolutionize various applications, from game playing and robotics to natural language processing and computer vision. Noteworthy papers in this area include: Internalizing World Models via Self-Play Finetuning for Agentic RL, which introduces a simple reinforcement learning framework that significantly improves performance in diverse environments. MARS: Reinforcing Multi-Agent Reasoning of LLMs through Self-Play in Strategic Games, which develops an end-to-end RL framework that incentivizes multi-agent reasoning and achieves strong strategic abilities. RL makes MLLMs see better than SFT, which investigates the impact of training strategies on multimodal language models and proposes a simple recipe for building strong vision encoders. VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents, which architecturally enforces and rewards the agent's reasoning process via reinforcement learning and achieves a 3x improvement over its untrained counterpart.