Core Insights - The article discusses the evolution of post-training paradigms in large models, particularly the shift from SFT+RLHF to a new two-stage approach involving RL Scaling and RL Alignment, which may enhance reasoning capabilities and model performance [3][4][5]. Summary by Sections Post-Training Paradigm Shift - The traditional two-stage post-training method of SFT+RLHF has been widely adopted since the release of GPT-3.5, providing a foundation for rapid convergence and instruction-following capabilities [3]. - The new paradigm suggests that large reasoning models may transition to a two-stage approach involving RL Scaling and RL Alignment, focusing on enhancing self-reflection and reasoning abilities without the need for a convergence foundation [4]. Advantages of the New Approach - RL Scaling can improve model performance on verifiable tasks like math and coding, while RL Alignment adjusts the model to align with human instructions and readability [4]. - This new method potentially mitigates reward hacking issues present in traditional post-training approaches, allowing for greater freedom in token search and enhancing reasoning capabilities [5]. Opportunities and Challenges - The shift to RL Scaling presents opportunities to explore how to utilize data without clear answers and to balance the difficulty of tasks to optimize learning [7]. - There are concerns regarding safety, as the enhanced capabilities from RL Scaling may lead to harmful reasoning emerging from the model, raising questions about the effectiveness of the RL Alignment phase in ensuring safety [6][7]. Generalization and Transferability - The performance improvements seen in math and coding tasks can be generalized to other types of tasks, indicating a broader applicability of the new model capabilities [5]. - Despite the advancements, there remains a preference for models like GPT-4o that excel in understanding user intent and following instructions, highlighting the importance of effective communication and efficiency in practical applications [7].