DeepSeek-V3 Cross-Domain Finetuning Trade-offs on GPQA Diamond Inference Efficiency

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the inference efficiency trade-off when applying cross-domain finetuning to DeepSeek-V3 on GPQA Diamond tasks. We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the inference efficiency trade-off when applying cross-domain finetuning to DeepSeek-V3 on GPQA Diamond tasks?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.