# Instruction Fine-Tuning: Does Prompt Loss Matter?

arXiv: https://arxiv.org/abs/2401.13586 · HTML: https://mlsanity.com/paper/2401.13586/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [RL with KL penalties is better viewed as Bayesian inference](https://mlsanity.com/paper/2205.11275/) (2022)
- [Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective](https://mlsanity.com/paper/2311.15792/) (2023)
- [Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users](https://mlsanity.com/paper/2311.05903/) (2023)

## Built on by (most cited first, 26 of 26)

- [Instruction Tuning With Loss Over Instructions](https://mlsanity.com/paper/2405.14394/) (2024)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [ModernVBERT: Towards Smaller Visual Document Retrievers](https://mlsanity.com/paper/2510.01149/) (2025)
- [MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector](https://mlsanity.com/paper/2408.08661/) (2024)
- [ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models](https://mlsanity.com/paper/2509.21826/) (2025)
- [TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation](https://mlsanity.com/paper/2603.00025/) (2026)
- [Data-efficient pre-training by scaling synthetic megadocs](https://mlsanity.com/paper/2603.18534/) (2026)
- [Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence](https://mlsanity.com/paper/2506.23908/) (2025)
- [Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://mlsanity.com/paper/2411.09539/) (2024)
- [Toward Cybersecurity-Expert Small Language Models](https://mlsanity.com/paper/2510.14113/) (2025)
- [Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health Counseling](https://mlsanity.com/paper/2503.03607/) (2025)
- [In-Context Probing for Membership Inference in Fine-Tuned Language Models](https://mlsanity.com/paper/2512.16292/) (2025)
- [Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval](https://mlsanity.com/paper/2601.13969/) (2026)
- [Facilitating Cognitive Accessibility with LLMs: A Multi-Task Approach to Easy-to-Read Text Generation](https://mlsanity.com/paper/2510.00662/) (2025)
- [PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models](https://mlsanity.com/paper/2603.05776/) (2026)
- [Improving Multilingual Math Reasoning for African Languages](https://mlsanity.com/paper/2505.19848/) (2025)
- [Supervised In-Context Fine-Tuning for Generative Sequence Labeling](https://mlsanity.com/paper/2509.00921/) (2025)
- [KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models](https://mlsanity.com/paper/2409.13749/) (2024)
- [Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models](https://mlsanity.com/paper/2506.13300/) (2025)
- [High-quality generation of dynamic game content via small language models: A proof of concept](https://mlsanity.com/paper/2601.23206/) (2026)
- [Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision](https://mlsanity.com/paper/2604.02812/) (2026)
- [Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices](https://mlsanity.com/paper/2606.19528/) (2026)
- [Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet](https://mlsanity.com/paper/2607.13430/) (2026)
- [PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning](https://mlsanity.com/paper/2607.18199/) (2026)
- [Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Instruction Tuning](https://mlsanity.com/paper/2609.22943/) (2026)
- [Decoupling Token Roles in Autoregressive Pretraining](https://mlsanity.com/paper/2609.33405/) (2026)

## 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.