Alignment Techniques and Robustness of Qwen14B on GSM8K-V Under Adversarial Noise

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent do alignment techniques (e.g., instruction tuning vs. reinforcement learning from human feedback) improve the robustness of Qwen14B on GSM8K-V when exposed to adversarial visual noise,. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

To what extent do alignment techniques (e.g., instruction tuning vs. reinforcement learning from human feedback) improve the robustness of Qwen14B on GSM8K-V when exposed to adversarial visual noise, and how does this compare to models like LLaVA-1.5 and InstructBLIP?

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