mlsanityData to 7 Oct 2026

Instruction Fine-Tuning: Does Prompt Loss Matter?

arXiv 2401.13586 ↗First posted 24 Jan 2024cs.LG cs.AI cs.CLcited by 26

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Abstract (arXiv)

We present a novel study analyzing the effects of various prompt loss token weights (PLW) for supervised instruction fine-tuning (SIFT). While prompt-masking (PLW = 0) is common for SIFT, some fine-tuning APIs support fractional PLWs and suggest that using a small non-zero PLW can help stabilize learning when fine-tuning on short-completion data. However, there has never been a study confirming this claim, and OpenAI, a major cloud-based SIFT provider, recently removed this parameter from their fine-tuning API. We found that performance of models fine-tuned on short-completion data had a statistically-significant negative quadratic relationship with PLW. Using small values (0.01 - 0.5) of PLW produced better results on multiple-choice and short-generation benchmarks (outperforming models fine-tuned on long-completion data) while large values (~ 1.0) of PLW produced better results on long-generation benchmarks. We explained this effect and verified its importance through additional experiments. This research serves as a warning to API providers about the importance of providing a PLW parameter for SIFT.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

12 of 31 references.

References · 12

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  6. 2020Learning to summarize from human feedbackcited by 2,562
  7. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,056
  8. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  9. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 311
  10. 2022RL with KL penalties is better viewed as Bayesian inferencecited by 130
  11. 2023Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspectivecited by 16
  12. 2023Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Userscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 11; 2026: 11.

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  1. 2024Instruction Tuning With Loss Over Instructionscited by 40
  2. 2025Navigating Rifts in Human-LLM Grounding: Study and Benchmarkcited by 27
  3. 2025ModernVBERT: Towards Smaller Visual Document Retrieverscited by 19
  4. 2024MIA-Tuner: Adapting Large Language Models as Pre-training Text Detectorcited by 13
  5. 2025ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Modelscited by 9
  6. 2026Data-efficient pre-training by scaling synthetic megadocscited by 7
  7. 2025Beyond Statistical Learning: Exact Learning Is Essential for General Intelligencecited by 6
  8. 2026TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generationcited by 5
  9. 2024Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guidecited by 4
  10. 2025Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health Counselingcited by 3
  11. 2025Toward Cybersecurity-Expert Small Language Modelscited by 3
  12. 2025In-Context Probing for Membership Inference in Fine-Tuned Language Modelscited by 3
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  1. 2025Facilitating Cognitive Accessibility with LLMs: A Multi-Task Approach to Easy-to-Read Text Generationcited by 2
  2. 2026Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrievalcited by 2
  3. 2025Improving Multilingual Math Reasoning for African Languagescited by 1
  4. 2025Supervised In-Context Fine-Tuning for Generative Sequence Labelingcited by 1
  5. 2026PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Modelscited by 1
  6. 2024KodeXv0.1: A Family of State-of-the-Art Financial Large Language Modelscited by 0
  7. 2025Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Modelscited by 0
  8. 2026High-quality generation of dynamic game content via small language models: A proof of conceptcited by 0
  9. 2026Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervisioncited by 0
  10. 2026Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devicescited by 0
  11. 2026Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leafletcited by 0
  12. 2026PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoningcited by 0
  13. 2026Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Efficient Instruction Tuningcited by 0
  14. 2026Decoupling Token Roles in Autoregressive Pretrainingcited by 0