Sebastian Raschka万字年终复盘：2025，属于「推理模型」的一年

Core Insights - The AI field continues to evolve rapidly, with significant advancements in reasoning models and algorithms such as RLVR and GRPO, marking 2025 as a pivotal year for large language models (LLMs) [1][4][19] - DeepSeek R1's introduction has shifted the focus from merely stacking parameters to enhancing reasoning capabilities, demonstrating that high-performance models can be developed at a fraction of previously estimated costs [9][10][12] - The importance of collaboration between humans and AI is emphasized, reflecting on the boundaries of this partnership and the evolving role of AI in various tasks [1][4][66] Group 1: Reasoning Models and Algorithms - The year 2025 has been characterized as a "year of reasoning," with RLVR and GRPO algorithms gaining prominence in the development of LLMs [5][19] - DeepSeek R1's release showcased that reasoning behavior can be developed through reinforcement learning, enhancing the accuracy of model outputs [6][19] - The estimated training cost for the DeepSeek R1 model is significantly lower than previous assumptions, around $5.576 million, indicating a shift in cost expectations for advanced model training [10][12] Group 2: Focus Areas in LLM Development - Key focus areas for LLM development have evolved over the years, with 2025 emphasizing RLVR and GRPO, following previous years' focus on RLHF and LoRA techniques [20][22][24] - The trend of "Benchmaxxing" has emerged, highlighting the overemphasis on benchmark scores rather than real-world applicability of LLMs [60][63] - The integration of tools in LLM training has improved performance, allowing models to access external information and reduce hallucination rates [54][56] Group 3: Architectural Trends - The architecture of LLMs is converging towards using mixture of experts (MoE) layers and efficient attention mechanisms, indicating a shift towards more scalable and efficient models [43][53] - Despite advancements, traditional transformer architectures remain prevalent, with ongoing improvements in efficiency and engineering adjustments [43][53] Group 4: Future Directions - Future developments are expected to focus on expanding RLVR applications beyond mathematics and coding, incorporating reasoning evaluation into training signals [25][27] - Continuous learning is anticipated to gain traction, addressing challenges such as catastrophic forgetting while enhancing model adaptability [31][32] - The need for domain-specific data is highlighted as a critical factor for LLMs to establish a foothold in various industries, with proprietary data being a significant concern for companies [85][88]