闭源越跑越快之后，DeepSeek V3.2 如何为开源模型杀出一条新路

Core Viewpoint - The article emphasizes that closed-source models are increasingly outperforming open-source models in complex tasks, with the performance gap widening over time [1]. Group 1: Key Issues with Open-Source Models - Open-source models face three critical issues: reliance on Vanilla Attention mechanisms limits computational efficiency in long-sequence scenarios, insufficient computational resources during post-training phases restrict performance on difficult tasks, and significant lag in generalization and instruction-following capabilities compared to closed-source systems [2]. Group 2: DeepSeek's Innovations - DeepSeek introduced two new models, DeepSeek V3.2 and DeepSeek V3.2 Speciale, which address the aforementioned issues through three improvements: the introduction of a highly efficient attention mechanism called DSA (DeepSeek Sparse Attention) to reduce computational complexity, a stable and scalable reinforcement learning protocol to significantly increase computational resources during post-training, and a new data pipeline to enhance generalization and instruction-following capabilities in AI agent scenarios [2][3]. Group 3: DSA Mechanism - The DSA mechanism reduces the complexity of core attention from O(L^2) to O(L*k), where k is much smaller than L, thus maintaining model performance even in long-context scenarios [11]. The DSA employs a two-stage sparsification mechanism that transforms full computation into selective computation, enhancing efficiency [7][10]. Group 4: Reinforcement Learning Strategy - DeepSeek V3.2 allocates over 10% of the computational budget to post-training, exceeding pre-training costs, and employs a mixed reinforcement learning approach to optimize performance [12][14]. This strategy combines reasoning, agent, and human alignment tasks into a single RL phase to mitigate catastrophic forgetting common in traditional multi-stage training [14]. Group 5: Impact on Open-Source Ecosystem - DeepSeek's advancements demonstrate that significant improvements in model performance can be achieved without relying on closed-source systems, suggesting a shift back to a more research-driven era in large model development. The company sets a precedent for the open-source community on how to innovate within limited budgets and reshape agent systems [16].