The field of artificial intelligence is moving towards developing more efficient reasoning and inference models. Researchers are focusing on reducing the computational cost and latency of these models while maintaining their accuracy. One of the key directions is the use of reinforcement learning to optimize the models' performance and encourage more intelligent responses. Another area of research is the development of novel architectures and algorithms that can efficiently handle long-context reasoning and inference. Notable papers in this area include: DLER, which achieves state-of-the-art accuracy-efficiency trade-offs by using a simple truncation length penalty and addressing key challenges in reinforcement learning optimization. Towards Flash Thinking via Decoupled Advantage Policy Optimization, which proposes a novel RL framework to reduce inefficient reasoning and achieves significant reductions in sequence length while outperforming the base model in overall accuracy. Every Attention Matters, which presents a hybrid architecture that integrates linear attention and softmax attention, reducing inference cost and computational overhead in long-context inference scenarios. DiffAdapt, which introduces a lightweight framework for difficulty-adaptive reasoning, enabling token-efficient LLM inference and reducing token usage by up to 22.4% while maintaining accuracy.