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Instruction Quality Matters: Refining Instructions for Effective Preference Learning

Seohyeong Lee, Hwaran Lee, Buru Chang

arXiv:2608.26779v1cs.CL

TL;DR

Preference learning is limited by the quality of the instructions that generate response pairs, because weak instructions restrict useful candidate responses and preference contrasts. The paper refines weak instructions using reward signals and rubric-guided LLM feedback, finding broad alignment gains across offline and online settings. Instruction refinement therefore complements response-centric curation by improving the candidate pool without discarding examples.

  • Problem

    Existing preference-learning analyses emphasize response quality and annotation reliability while treating instructions as fixed, despite their role in shaping candidate-response distributions.

  • Method

    The pipeline identifies weak instructions with reward signals, revises them using rubric-guided LLM feedback, and regenerates candidate responses without discarding examples.

  • Results

    Across offline and online preference learning, instruction refinement yields broad alignment gains across multiple models, objectives, and benchmarks.

  • Takeaways & Limitations

    Instruction quality is an important factor in preference-learning effectiveness beyond response quality and optimization objectives.

  • Takeaways & Limitations

    The approach depends on reward-model calibration and biases, can be sensitive to refinement thresholds, adds computational cost, and is limited to text-only models.

Abstract

from arXiv · show

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/

1 Introduction

Preference learning depends not only on response and annotation quality but also on the instructions that induce candidate-response distributions. The paper identifies instruction quality as an upstream factor and introduces refinement to improve preference data and alignment.

  • Ambiguous, incomplete, or underspecified instructions can produce few high-quality responses and many trivial failures, limiting response-centric fixes.
  • Best-of-N and Worst-of-N analyses find that high-quality instructions improve both the best and worst sampled responses, increasing useful candidate coverage.
  • The proposed pipeline selects weak instructions using reward signals and revises them with rubric-guided LLM feedback without discarding examples.
  • Across offline and online settings, instruction refinement produces broad alignment gains of up to 8%p across multiple backbones, objectives, and benchmarks.
  • Further analyses show improved instruction-quality scores and response rewards, higher Best-of-N ceilings, robustness to thresholds, and benefits over generic rewriting.
  • Instruction quality shapes response-quality ceilings and floors, affecting the informativeness of pairwise preference data.

2 Related Work

Prior preference-data improvement methods mainly curate responses or pairs, while related instruction-focused methods target instruction tuning or robustness. This work instead refines instructions to elicit clearer preference signals.

  • Existing preference-learning data methods typically select informative or reliable pairs using reward margins, response quality, or annotation consistency.
  • Self-refinement and related data-improvement pipelines primarily refine responses, agent behavior, or datasets while leaving instructions fixed.
  • Instruction-selection studies use prompt quality or instruction-following difficulty for instruction tuning rather than preference-data curation.
  • Figure 2 frames instruction quality as affecting both response distributions and preference-pair informativeness through Best-of-N and Worst-of-N rewards.
  • Unlike robustness studies that examine sensitivity to input variation, this work refines low-quality instructions to construct more informative preference data.

3 Instruction Quality and Refinement

The paper studies how instruction quality shapes sampled response distributions and preference-gradient informativeness, then refines weak instructions before preference-pair construction. Its procedure uses reward thresholds and rubric-guided feedback to improve candidate pools while retaining examples.

  • 3.1 Motivating Analysis: Instruction Quality Shapes Response Distributions: Best-of-N and Worst-of-N estimate response-quality ceilings and floors under fixed sampling budgets, using 1.5K UltraFeedback instructions grouped by quality.
  • 3.1 Motivating Analysis: Instruction Quality Shapes Response Distributions: The analysis assumes explicit reward-model scores preserve relevant ordering and margin trends of implicit rewards emerging during preference optimization.
  • 3.1 Motivating Analysis: Instruction Quality Shapes Response Distributions: High-quality instructions achieve higher Best-of-N and Worst-of-N rewards, while low-quality instructions restrict strong candidates and increase trivial failures.
  • 3.1 Motivating Analysis: Instruction Quality Shapes Response Distributions: In the DPO interpretation, stronger chosen targets and less-degenerate rejected responses make preference contrasts more useful for optimization.
  • 3.2 Refining Instructions for Preference Data: Refinement occurs before constructing preference pairs to improve the candidate response pool rather than modifying responses after generation.
  • 3.2 Refining Instructions for Preference Data: The refinement loop generates two on-policy responses, computes rewards, and revises instructions when the minimum reward falls below threshold τ, repeating until success or iteration limit.
  • 3.2 Refining Instructions for Preference Data: Instruction refinement adds two API calls per sample, costing about $0.0026 per sample in the reported setup, with potentially substantial overhead at larger scales.
  • 3.3 Evaluation and Refinement Criteria: Rubrics provide structured diagnostic feedback covering clarity, specificity, completeness, safety, answerability, conciseness, and format consistency.

4 Experiments

The experiments evaluate instruction refinement in offline and online preference learning across multiple models, objectives, benchmarks, and improvement baselines. Refinement generally improves alignment while preserving the underlying learning algorithms, although gains are not uniform across metrics.

  • 4.1 Offline Preference Learning: Offline evaluation tests instruction refinement on UltraFeedback across two backbones, DPO and SimPO, and three alignment benchmarks.The evaluated benchmarks are MT-Bench, Evol-Instruct, and AlpacaEval.
  • 4.1 Offline Preference Learning: Refined datasets yield broad gains over original datasets across objectives, backbones, and benchmarks, especially on AlpacaEval LC win rate.The comparison covers DPO and SimPO on both LLaMA3-8B and Mistral-7B backbones.
  • 4.1 Offline Preference Learning: Instruction refinement achieves the strongest overall performance among the compared dataset-improvement interventions.The baselines include CoT prompting, generic paraphrasing, Self-Refine, and response-level data selection.
  • 4.2 Online Preference Learning: Online experiments apply one refinement step before each response-generation round while leaving the underlying preference-learning algorithm unchanged.The setting includes SPA and iterative DPO-style pipelines with evolving policies and data distributions.
  • 4.2 Online Preference Learning: Online refinement improves most preference-learning variants, with the strongest gains for SPA at iteration 3 and consistent AlpacaEval LC win-rate improvements.These gains occur despite continual updates to the policy generating new data.
  • 4.2 Online Preference Learning: The gains are broad but not uniform: MT-Bench scores slightly decrease for reward-model and implicit-reward variants despite improved or maintained win rates.This limitation is associated with online variants rather than all reported metrics.

5 Discussion

Additional analyses examine reward-model dependence, response-quality distributions, threshold sensitivity, rubric effects, and preservation of task intent. Together, they support instruction refinement as a robust but task- and threshold-dependent form of preference-data improvement.

  • 5 Discussion: Refined-OSSAT generally outperforms Original-OSSAT when a different reward model assigns preference labels, though gains are not uniform across metrics.ArmoRM selects instructions for refinement, while OSSAT assigns preference labels.
  • 5.1 Cross-Reward Model Experiment: Best-of-N and Worst-of-N curves shift upward after refinement, indicating higher response-quality ceilings and improved floors, especially for low-quality instructions.The analysis uses a fixed sampling budget and compares High-X with Low-X instruction groups.
  • 5 Discussion: Refinement generally outperforms the non-refined baseline across minimum-reward thresholds, with moderate thresholds providing the most consistent gains.DPO performs best around τ = 0.13, while SimPO degrades at higher thresholds, likely from overly aggressive refinement reducing response diversity.
  • 5.4 Rubric Effect Analysis: Rubric-guided refinement provides gains beyond general GPT-4o rewriting, supporting a distinct contribution from structured diagnostic feedback.The ablation controls the refine model, prompt guidance, and one-pass refinement setting.
  • 5.4 Rubric Effect Analysis: Rubric-based improvements in safety, answerability, and clarity correlate more positively with downstream reward gains than rubric-free improvements.Specificity and completeness show near-zero or negative correlations with reward gains.
  • 5 Discussion: Most refined instructions preserve the original core task, and instruction-following difficulty changes only slightly after refinement.Human evaluation reports 88.4% preservation of core task intent.

6 Conclusion

Instruction quality limits achievable response quality and preference-learning performance. Refining instructions strengthens preference signals without discarding data across offline and online settings.

  • Instruction quality can constrain achievable response quality and, consequently, preference-learning performance.
  • Instruction refinement strengthens preference signals without discarding preference data.
  • Across multiple algorithms and benchmarks, instruction refinement consistently improves preference-data quality and alignment performance.

Limitation

The study’s limitations concern reward-model dependence, refinement sensitivity, computational cost, and scope restricted to text-only language models.

  • Reward-model calibration and biases may affect the effectiveness of identifying and refining low-quality instructions.
  • Aggressive refinement can degrade performance in some settings, making results sensitive to threshold selection.
  • Instruction refinement adds computational cost through iterative feedback and rewriting, although it is generally more cost-efficient than collecting new preference data.
  • The study is limited to text-only language models, leaving multimodal extension as future work.

A Motivating Analysis Details

Instruction quality shapes both tails of the response-quality distribution from which preference pairs are sampled. Best-of-N and Worst-of-N analyses motivate refinement as an upstream intervention to improve candidate coverage and preference informativeness.

  • Improving both response-quality tails increases strong chosen candidates and reduces degenerate rejected candidates that yield overly easy preference pairs.For K candidates, the high-quality and trivial-response probabilities are determined by the upper and lower-tail masses of the instruction-induced distribution.
  • Best-of-N and Worst-of-N analysis: At N = 16, high-quality instructions achieve a mean BoN reward of 0.150 versus 0.128 for low-quality instructions, a gap of 0.0218 (p < 0.0001).The experiment uses 1.5K UltraFeedback instructions divided into High, Mid, and Low quality groups, with multiple LLaMA3-8B responses generated per instruction.
  • Best-of-N and Worst-of-N analysis: BoN estimates the response-quality ceiling under a fixed sampling budget, whereas WoN characterizes the quality floor induced by an instruction.
  • Best-of-N and Worst-of-N analysis: High-quality instructions improve both upper and lower tails of the sampled response-quality distribution.
  • Preference-signal implications: In DPO, scarce high-quality chosen responses limit positive updates, while smaller chosen–rejected quality gaps preserve more informative gradients.
  • Instruction refinement criteria: The refinement framework derives seven instruction rubrics covering criteria including clarity, specificity, completeness, safety, answerability, conciseness, and format consistency.

B.3 Benchmarks

The experiments evaluate alignment with LLM-as-a-judge benchmarks across offline and online settings, including module substitution and alternative reward models. Refined data generally outperforms original data across nearly all reported benchmarks, while improvement magnitude depends on the reward signal.

  • Benchmarks: MT-Bench, Evol-Instruct, and AlpacaEval 2.0 report LLM-as-a-judge win rates, with MT-Bench and Evol-Instruct also reporting averaged single-response scores.
  • Benchmarks: MT-Bench evaluates two-turn multi-turn dialogue and instruction execution, while Evol-Instruct targets instruction-following beyond relatively simple benchmark prompts.
  • Benchmarks: AlpacaEval compares target-model responses with GPT-4-1106-preview references and reports target win rates judged by GPT-4o-mini-2024-07-18.
  • Experimental settings: Offline experiments use DPO and SimPO, while online experiments follow SPA with one epoch of DPO training per iteration.
  • Refinement modules: A supervised-fine-tuned feedback and refiner module produces superior training performance across nearly all benchmarks, consistent with foundation-model refinement results.
  • Reward-model robustness: Using an alternative reward model shows that improvement magnitude and consistency are affected by the reward signal.

D.3 Alternative Dataset Experiment

Across alternative-domain, offline, and online evaluations, instruction refinement generally improves preference-learning outcomes and response-quality distributions. Gains are strongest with selective refinement at moderate thresholds, while excessive refinement can reduce performance.

  • Alternative Dataset Experiment: Refinement improves the medical-domain preference-learning trend, with particularly clear gains on QA-style benchmarks.The refined model scores higher on MedQA, MedMCQA, and PubMedQA, although effects vary across MMLU-med.
  • Response-Quality Distribution: The average minimum reward score rises from µy = 0.100 to µy = 0.114 after refinement.This shift corresponds to 11.65% of the effective reward range of ArmoRM scores on UltraFeedback.
  • Response-Quality Distribution: Refined instructions reduce low-quality generations and more often elicit consistently high-quality responses.The distributional shift helps explain improvements in offline and online preference-learning performance.
  • Online Preference Learning: Across two SPA iterations, instruction refinement consistently improves or maintains performance over non-refined counterparts in online preference learning.Reward-model-based and implicit-reward-based settings particularly benefit on Evol-Instruct and AlpacaEval.
  • Online Preference Learning: Instruction refinement accelerates performance improvements as online training progresses rather than benefiting only initialization.The trend is observed in most SPA and implicit-reward-based settings across training iterations.
  • Threshold Sensitivity: Moderate minimum-reward thresholds around τ = 0.1–0.13 generally improve performance, whereas τ = 0.15 sometimes degrades it.The results span MT-Bench, Evol-Instruct, and AlpacaEval for both DPO and SimPO.

E.4 IFD Score Analysis

The IFD analysis tests whether refinement gains could instead reflect easier instructions. Refinement slightly increases instruction-following difficulty, while gains persist across evaluation-difficulty groups and repeated refinement iterations.

  • Motivation: Instruction refinement could confound instruction-quality gains with reduced task difficulty.The analysis therefore treats task difficulty as an alternative explanation for downstream performance improvements.
  • IFD Analysis: Mean IFD increases from 0.6423 to 0.6823, with a mean paired difference of +0.0406 after refinement.Using Llama-3-Base-8B-SFT, 54.38% of instances have higher IFD scores after refinement.
  • IFD Analysis: The higher IFD scores indicate that refined instructions are marginally more difficult, making reduced task difficulty an unlikely explanation for performance gains.The reported increase is limited in magnitude.
  • Benchmark Evaluation: Refined models outperform original models across most IFD-based evaluation groups on Evol-Instruct and MT-Bench.The gains include prompts with relatively high IFD scores, not only easier evaluation prompts.
  • Repeated Refinement: Performance generally improves from SFT through later refinement iterations across MT-Bench, Evol-Instruct, and AlpacaEval.Gains are largest in early iterations, while later iterations show smaller improvements or signs of saturation.

F.1 Task/Distribution Shift Human Evaluation

Human evaluation finds that refinement usually preserves the original task while occasionally shifting intent, and reward-model decisions show moderate agreement with human judgments. A degradation case highlights instability on strict-format tasks.

  • Task/Intent Preservation: 88.4% of evaluated instructions preserve the core task, combining 66.7% Same Intent with 21.7% Minor Shift.The evaluation covered 60 instructions assessed by three annotators each; 8.3% had major shifts and 3.3% were ties.
  • Task/Intent Preservation: Major shifts can redirect unsafe or unanswerable instructions toward safer or answerable tasks.Examples include replacing dangerous tool use with safety guidance and adding a missing target word requirement.
  • Human Alignment: Reward-model refinement selection agrees with human majority judgments in 76.7% of cases, while preference labels agree in 71.4%.The evaluation used 30 instruction–response-pair tuples with three human evaluators per tuple.
  • Human Alignment: Reward models may remain biased by response length, style, and surface-level fluency.The paper therefore treats the reward model as reasonably aligned with, but not equivalent to, human judgment.
  • Degradation Case Study: Strict-format tasks can degrade after refinement when the refined model adds non-requested content or misclassifies entities.A named entity extraction example shows lower evaluation scores, whereas this pattern is less evident in open-ended generation.
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