Reference library
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
Paper summary
DeepSeek-V3.2 combines sparse attention, scaled reinforcement learning, and synthesized agent tasks. The report argues these changes improve long-context efficiency and evaluated reasoning and tool-use performance.
Relevance to the project
A reference for measuring selective routing and the provenance of generated training tasks in optional language-model work.
Performance comparisons are claims for the preserved report version, not current rankings.
Source record
- Reference ID
- deepseek-v32
- Archived version
- 2512.02556v1
- Summary provenance
- reference-library/papers/deepseek-v32/metadata.json · summary
- Review status
- initial orientation summary; not a critical full-paper review
- Reading scope
- Selected abstract/opening and available closing discussion/conclusion passages in the preserved text extract; broader project notes provide context. No reproduction of experiments, exhaustive methods review, or equation/figure audit.