The field of large language models is moving towards the development of more advanced multi-agent reasoning systems. These systems employ multiple agents to iteratively refine solutions, offering a promising alternative to traditional single-agent approaches. Recent innovations have focused on improving the efficiency and effectiveness of these systems, including the introduction of novel reinforcement learning frameworks and fine-grained credit assignment mechanisms. Notably, the use of agentic pipeline parallelism and retrospective critic mechanisms has shown significant potential in advancing multi-agent reasoning systems. Furthermore, research has highlighted the importance of initializing large language models with diverse, high-quality reasoning primitives to achieve stable and sample-efficient reinforcement learning training. The development of rubric-driven RL frameworks has also enabled large language models to receive dense and informative rewards, leading to improved performance in multi-domain reasoning tasks. Overall, the field is witnessing a shift towards more sophisticated and specialized training methods, including the use of hierarchical extensions of Group Relative Policy Optimization and decoupled training pipelines. Some noteworthy papers in this regard include MarsRL, which introduces a novel reinforcement learning framework with agentic pipeline parallelism, and CriticSearch, which proposes a fine-grained credit-assignment framework via a retrospective critic mechanism. Additionally, Tailored Primitive Initialization has been shown to be essential for achieving stable and sample-efficient RL training, and Reward and Guidance through Rubrics has demonstrated the potential of rubric-driven RL frameworks in improving multi-domain reasoning performance.