Direct Preference Optimization and RLHF Throughput in Adversarial Code Generation on HEIGER

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the throughput of DPO compare to RLHF when evaluating LLMs on the HEIGER benchmark for adversarial code generation tasks with varying model sizes. Large language models (LLMs) based on transformer architectures are typically described through collections of architectural components and training procedures, obscuring their underlying computational structure. This review article provides a concise mathematical reference for. 17 claims were extracted from source literature; 15 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the throughput of DPO compare to RLHF when evaluating LLMs on the HEIGER benchmark for adversarial code generation tasks with varying model sizes?

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