The field of artificial intelligence is witnessing significant advancements in multimodal intelligence and reinforcement learning. Researchers are exploring novel approaches to improve the robustness and efficiency of reinforcement learning from human feedback (RLHF) by leveraging techniques such as Mixture-of-Experts (MoE) reward models and hierarchical process reward models. These innovations aim to mitigate issues like reward hacking and over-optimization, which are critical challenges in RLHF. Furthermore, the development of agentic multimodal models, such as Skywork-R1V4 and ARM-Thinker, is enabling more sophisticated and generalizable perception policies. These models are capable of performing complex tasks like spatial reasoning, visual hallucination, and embodied AI. Noteworthy papers in this area include the proposal of an upcycle and merge MoE reward modeling approach, which effectively mitigates reward hacking, and the introduction of Artemis, a perception-policy learning framework that performs structured proposal-based reasoning. Additionally, the development of SPARK, a three-stage framework for reference-free reinforcement learning, and Argos, a principled reward agent for training multimodal reasoning models, are also notable contributions.