The field of artificial intelligence is witnessing significant advancements in the development of efficient language models and reinforcement learning algorithms. Researchers are focusing on creating models that can achieve state-of-the-art performance while reducing computational resources and improving explainability. Novel training pipelines and architectures are being proposed to address the challenges of training reasoning-capable models in specialized domains. Noteworthy papers include Gazal-R1, which presents a parameter-efficient two-stage training pipeline for medical reasoning, and M3PO, which introduces a scalable model-based reinforcement learning framework. HyperCLOVA X THINK is also notable for its competitive performance on Korea-focused benchmarks, while Jan-nano achieves remarkable efficiency through radical specialization. TD-MPC-Opt presents a novel approach to knowledge transfer in model-based reinforcement learning, enabling the distillation of large world models into compact models.