Recent advancements in large language models (LLMs) are addressing their limitations, such as computational expense and adaptability to new tasks. Configurable foundation models are emerging to overcome these challenges. Techniques like chunked prefill and margin generation are improving LLM inferencing performance on long-context tasks, achieving an average 7.5% accuracy improvement in reasoning skills. Additionally, methods to extend context post-training are being explored, which are crucial for models like OpenAI o1. Making text embedders few-shot learners is also being discussed. A two-stage algorithm called ARES, combining reinforcement learning and supervised fine-tuning, has shown a 70% win rate in rationale reasoning against baselines and a 2.5% average increase in performance.