mlsanityData to 13 Oct 2026

Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv 2608.26779 ↗First posted 27 Aug 2026cs.CLcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which they are generated. We identify instruction quality as a hidden bottleneck in preference learning: low-quality or ambiguous instructions restrict the response-quality distribution, limiting strong chosen responses and weakening preference signals. Through Best- and Worst-of-N analyses, we show that instruction quality constrains both the ceiling and floor of sampled response quality. Motivated by this observation, we introduce an instruction-refinement pipeline that selects weak instructions using reward signals and revises them with rubric-guided LLM feedback, improving preference data without discarding examples. Across offline and online preference learning settings, experiments on multiple models and benchmarks show broad alignment improvements over original data and alternative data-improvement strategies. Further analyses indicate that instruction refinement raises achievable response quality and complements response-centric preference data curation. Overall, instruction quality emerges as a key factor governing how informative preference signals are formed for LLM alignment. Code is available at: https://github.com/01choco/instruction-refinement/

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 21 of its 40 references that are in the map. First posted on arXiv 27 Aug 2026.

Builds on

21 of 40 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025What Matters in Data for DPO?23 Aug
  2. 2024Self-Boosting Large Language Models with Synthetic Preference Data9 Oct
  3. 2024Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora23 Aug
  4. 2024Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment6 Jun
  5. 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
  6. 2023LIMA: Less Is More for Alignment18 May

Other references · 15

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,492
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,986
  3. 2017Deep reinforcement learning from human preferencescited by 3,871
  4. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,011
  5. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,228
  6. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,450
  7. 2024DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Modelcited by 1,115
  8. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,031
  9. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 553
  10. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 398
  11. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Expertscited by 371
  12. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 303
3 more
  1. 2025reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputscited by 25
  2. 2024ReIFE: Re-evaluating Instruction-Following Evaluationcited by 22
  3. 2024FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Onlycited by 11

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Close in wording, no citation either way

  1. 2024A Systematic Examination of Preference Learning through the Lens of Instruction-Following
  2. 2025Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks
  3. 2026Cross-Relational Preference Learning for Better LLM Instruction Following
  4. 2025BLEUBERI: BLEU is a surprisingly effective reward for instruction following
  5. 2024Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
  6. 2026Bidirectional Preference Synthesis: Learning Prompt-Conditioned Preferences from Boundary Failures
  7. 2025Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
  8. 2025ProDS: Preference-oriented Data Selection for Instruction Tuning