Auxiliary-Loss-Free Load Balancing in DeepSeek-V3 for Stable Code Generation

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the auxiliary-loss-free load balancing strategy in DeepSeek-V3 influence model performance stability on code generation tasks in the GPQA Diamond domain compared to traditional MoE load. For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to routing collapse or increased computational overhead. Existing methods commonly employ an auxiliary loss to encourage load balance, but a large auxiliary loss will introduce non-negligible interference. 13 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the auxiliary-loss-free load balancing strategy in DeepSeek-V3 influence model performance stability on code generation tasks in the GPQA Diamond domain compared to traditional MoE load balancing methods?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance