Now, Scaling What?

Group 1 - The core viewpoint of the article revolves around the transition in the AI industry towards exploring "What to Scale" as the traditional Scaling Law faces diminishing returns, prompting researchers to seek new paradigms for enhancing model capabilities [3][4]. - The article highlights the emergence of new scaling targets, including "Self-Play RL + LLM," "Post-Training Scaling Law," and "Test-Time Training," as researchers aim to improve model performance beyond pre-training [4][6]. - A significant focus is placed on Test-Time Scaling (TTS), which involves increasing computational resources during the inference phase to enhance model output quality, marking a shift from pre-training to inference optimization [6][7]. Group 2 - The article discusses various scaling strategies, including Parallel Scaling, Sequential Scaling, Hybrid Scaling, and Internal Scaling, each with distinct methodologies aimed at improving model performance during testing [9][10]. - It emphasizes the equal importance of fine-tuning and inference in the post-training phase, suggesting that both aspects are crucial for adapting models to specific applications and enhancing their output quality [11].