Scaling Laws with Learning Rate Annealing in Multimodal Code Generation Benchmarks

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the proposed scaling law with learning rate annealing perform on multimodal code generation benchmarks like CoderBench or DeCompEval compared to traditional power-law scaling methods. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the proposed scaling law with learning rate annealing perform on multimodal code generation benchmarks like CoderBench or DeCompEval compared to traditional power-law scaling 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