# Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Models

arXiv: https://arxiv.org/abs/2510.27077 · HTML: https://mlsanity.com/paper/2510.27077/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment and jailbreak attacks, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Safety alignment and jailbreak attacks](https://mlsanity.com/alignment/safety-alignment-and-jailbreak-attacks/)
- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 13 of 13)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [Parameter-Efficient Fine-Tuning Design Spaces](https://mlsanity.com/paper/2301.01821/) (2023)
- [Complementary Relation Contrastive Distillation](https://mlsanity.com/paper/2103.16367/) (2021)
- [Faster Meta Update Strategy for Noise-Robust Deep Learning](https://mlsanity.com/paper/2104.15092/) (2021)
- [XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation](https://mlsanity.com/paper/2106.04563/) (2021)
- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs](https://mlsanity.com/paper/2509.18113/) (2025)
- [ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning](https://mlsanity.com/paper/2507.11500/) (2025)
- [High Fidelity Text to Image Generation with Contrastive Alignment and Structural Guidance](https://mlsanity.com/paper/2508.10280/) (2025)
- [PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training](https://mlsanity.com/paper/2507.14202/) (2025)

## Built on by (most cited first, 3 of 3)

- [Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models](https://mlsanity.com/paper/2512.06711/) (2025)
- [Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models](https://mlsanity.com/paper/2512.13980/) (2025)
- [Advancing Text Classification with Large Language Models and Neural Attention Mechanisms](https://mlsanity.com/paper/2512.09444/) (2025)

## Abstract (arXiv)

This paper addresses the limitations of large-scale language models in safety alignment and robustness by proposing a fine-tuning method that combines contrastive distillation with noise-robust training. The method freezes the backbone model and transfers the knowledge boundaries of the teacher model to the student model through distillation, thereby improving semantic consistency and alignment accuracy. At the same time, noise perturbations and robust optimization constraints are introduced during training to ensure that the model maintains stable predictive outputs under noisy and uncertain inputs. The overall framework consists of distillation loss, robustness loss, and a regularization term, forming a unified optimization objective that balances alignment ability with resistance to interference. To systematically validate its effectiveness, the study designs experiments from multiple perspectives, including distillation weight sensitivity, stability analysis under computation budgets and mixed-precision environments, and the impact of data noise and distribution shifts on model performance. Results show that the method significantly outperforms existing baselines in knowledge transfer, robustness, and overall safety, achieving the best performance across several key metrics. This work not only enriches the theoretical system of parameter-efficient fine-tuning but also provides a new solution for building safer and more trustworthy alignment mechanisms.