mlsanityData to 14 Oct 2026

LookAhead Tuning: Safer Language Models via Partial Answer Previews

arXiv 2503.19041 ↗First posted 24 Mar 2025cs.CL cs.AI cs.CVcited by 7

On the map: LLM alignment / RLHF / preference optimisation → Safety alignment and jailbreak attacks

also in 1 more
Abstract (arXiv)

Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often compromises their previously established safety alignment. To mitigate the degradation of model safety during fine-tuning, we introduce LookAhead Tuning, a lightweight and effective data-driven approach that preserves safety during fine-tuning. The method introduces two simple strategies that modify training data by previewing partial answer prefixes, thereby minimizing perturbations to the model's initial token distributions and maintaining its built-in safety mechanisms. Comprehensive experiments demonstrate that LookAhead Tuning effectively maintains model safety without sacrificing robust performance on downstream tasks. Our findings position LookAhead Tuning as a reliable and efficient solution for the safe and effective adaptation of LLMs.

Key facts. Cited by 7 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 24 of its 40 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

24 of 40 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deep10 Jun
  2. 2024Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications7 Feb
  3. 2023Removing RLHF Protections in GPT-4 via Fine-Tuning9 Nov
  4. 2023LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B31 Oct
  5. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!5 Oct
  6. 2023Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models4 Oct

Other references · 18

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2025s1: Simple test-time scalingcited by 1,341
  4. 2023LIMA: Less Is More for Alignmentcited by 1,100
  5. 2023An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningcited by 639
  6. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 535
  7. 2023Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructionscited by 387
  8. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learningcited by 257
  9. 2024SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decodingcited by 247
  10. 2021Towards Continual Knowledge Learning of Language Modelscited by 174
  11. 2024Marco-o1: Towards Open Reasoning Models for Open-Ended Solutionscited by 144
  12. 2023Regulating ChatGPT and other Large Generative AI Modelscited by 104
6 more
  1. 2024Revisiting Catastrophic Forgetting in Large Language Model Tuningcited by 99
  2. 2023Fine-tuning can cripple your foundation model; preserving features may be the solutioncited by 96
  3. 2024Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templatescited by 96
  4. 2024Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptationcited by 80
  5. 2024Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attackscited by 33
  6. 2025The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Modelscited by 31

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignmentcited by 43
  2. 2025The Rise of Small Language Models in Healthcare: A Comprehensive Surveycited by 18
  3. 2025Unforgotten Safety: Preserving Safety Alignment of Large Language Models with Continual Learningcited by 10
  4. 2026Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sinkcited by 4
  5. 2026How Does Prefix Matter in Reasoning Model Tuning?cited by 0
  6. 2026Refusing Intent, Not Form: Wrapper-Based Intent-Group Supervision for LLM Safetycited by 0
  7. 2026SafeTune: A Unified Faithful Library for Auditing and Repairing Safety Drift in Fine-Tuned LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging
  2. 2026Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance
  3. 2024Locking Down the Finetuned LLMs Safety
  4. 2025Gradient Surgery for Safe LLM Fine-Tuning
  5. 2024Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations
  6. 2025Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning
  7. 2025Alleviating the Fear of Losing Alignment in LLM Fine-tuning
  8. 2025Secure LLM Fine-Tuning via Safety-Aware Probing