Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

arXiv:2609.37837 · cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Tunanzzz/Meerkat-VL

Terminology

• Alignment — 40× in this paper · explained in 1 episode
• Distillation — 28× in this paper · explained in 2 episodes
• Self-distillation — 10× in this paper · explained in 6 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• On-Policy Distillation — 7× in this paper · explained in 3 episodes
• GRPO — 6× in this paper · explained in 6 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Group Relative Policy Optimization — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• On-policy Self-Distillation (OPSD) — 2× in this paper · explained in 2 episodes
• Think in Safety — 2× in this paper
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Anchor Loss — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Intent-Privilege On-Policy Self-Distillation — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Tenth International Conference on Learning Representations — 1× in this paper

Sources

• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
• FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts
• Distilling the Knowledge in a Neural Network
• CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment
• SGuard-v1: Safety Guardrail for Large Language Models
• HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model
• Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs
• MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?
• DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models
• MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models
• Safety Alignment for Vision Language Models
• SIA: Enhancing Safety via Intent Awareness for Vision-Language Models
• Qwen3 Technical Report
• SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Learning by Distilling Context
• MMAligner: Safeguarding Multimodal Large Language Models through Representation Calibration
• CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks
• SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model
• Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

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