Recent discussions in the field of artificial intelligence highlight advancements in reasoning-enhanced reward models and evaluation methods for large language models (LLMs). A paper titled 'Reinforcing Thinking through Reasoning-Enhanced Reward Models' co-authored by researchers from the University of California, Santa Cruz, The Harker School, and Meta, emphasizes a novel approach to improving LLM performance. Additionally, a new study from Carnegie Mellon University titled 'Predicting the Performance of Black-box LLMs through Self-Queries' has been introduced, focusing on self-query methods for evaluating LLMs. Another innovative method, PRIME, has been proposed as an open-source online reinforcement learning technique that utilizes implicit Process Reward Modelling (PRM) to enhance LLM reasoning capabilities, reportedly improving mathematical reasoning by up to 27%. Furthermore, a creative evaluation idea suggests using LLMs to assess other LLMs, with scores contributing to a page-rank style graph, though concerns about the reliability of the evaluation markers have been raised.