Oracle-RLAIF vs. SFT for Video Captioning on MSVD Across Model Scales

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does Oracle-RLAIF training compare to SFT in terms of video captioning accuracy and inference latency on the MSVD benchmark across 1B, 7B, and 13B parameter models. Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tuning (SFT) with instruction-tuned datasets, integrating LLM with visual encoders, and adding. 6 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does Oracle-RLAIF training compare to SFT in terms of video captioning accuracy and inference latency on the MSVD benchmark across 1B, 7B, and 13B parameter models?

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