DeepSeek V3.2's path to GPT-5-level performance: sparse attention, RL at scale, and context reuse

This post details the technical contributions of DeepSeek-V3.2, focusing on three key areas: 1) DeepSeek Sparse Attention (DSA) which uses a learned indexer and token selector to reduce the O(N^2) attention cost to O(N*k), enabling efficient processing of long contexts (128k). 2) Scaled Reinforcement Learning (RL) with GRPO, incorporating stability fixes and a significant RL budget (>10% of pre-training compute), leading to qualitatively new behaviors and improved reasoning. 3) Revised thinking retention for tool use, where reasoning traces persist across tool calls, reducing token inefficiency and latency in complex agentic workflows. The post highlights the intelligence vs. efficiency tradeoff, positioning DeepSeek-V3.2 as a cost-effective alternative for high-quality reasoning tasks.