The field of large language models is witnessing significant advancements with a focus on improving generalization and adaptability across diverse tasks. Researchers are exploring innovative approaches to reinforce learning, including the use of hybrid rewards, curriculum-based progression, and dynamic process reward modeling. These methods aim to enhance the performance of large language models by providing more nuanced and task-aware supervision. Notably, the integration of reinforcement learning with verifiable rewards and the use of rubrics as rewards are showing promising results in balancing objective and subjective evaluation criteria. Overall, the field is moving towards developing more generalizable and adaptable large language models that can excel in a wide range of tasks. Noteworthy papers include: Omni-Think, which introduces a unified reinforcement learning framework that enhances LLM performance across diverse tasks. Data Mixing Agent, which proposes a model-based framework that learns to re-weight domains for continual pre-training. Rubrics as Rewards, which uses structured rubrics as interpretable reward signals for on-policy training. Dynamic and Generalizable Process Reward Modeling, which features a reward tree to capture and store fine-grained reward criteria.