DeepSeek-V3.2 is a model that harmonizes high computational efficiency with superior reasoning and agent performance. Our approach is built upon three key technical breakthroughs:

1. DeepSeek Sparse Attention (DSA): an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios.

2. Scalable Reinforcement Learning Framework: By implementing a robust RL protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5.

3. Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, [DeepSeek team] developed a novel synthesis pipeline that systematically generates training data at scale. This facilitates scalable agentic post-training, improving compliance and generalization in complex interactive environments.

Reference

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models