# Learning or Self-aligning? Rethinking Instruction Fine-tuning

arXiv: https://arxiv.org/abs/2402.18243 · HTML: https://mlsanity.com/paper/2402.18243/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 19 of 19)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Self-Rewarding Language Models](https://mlsanity.com/paper/2401.10020/) (2024)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://mlsanity.com/paper/2312.09390/) (2023)
- [The False Promise of Imitating Proprietary LLMs](https://mlsanity.com/paper/2305.15717/) (2023)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://mlsanity.com/paper/2212.12017/) (2022)
- [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://mlsanity.com/paper/2306.16092/) (2023)
- [R-Tuning: Instructing Large Language Models to Say `I Don't Know'](https://mlsanity.com/paper/2311.09677/) (2023)
- [Tuning Language Models by Proxy](https://mlsanity.com/paper/2401.08565/) (2024)
- [Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning](https://mlsanity.com/paper/2305.09246/) (2023)
- [Rethinking Data Selection for Supervised Fine-Tuning](https://mlsanity.com/paper/2402.06094/) (2024)
- [Human-Instruction-Free LLM Self-Alignment with Limited Samples](https://mlsanity.com/paper/2401.06785/) (2024)
- [Ambiguity-Aware In-Context Learning with Large Language Models](https://mlsanity.com/paper/2309.07900/) (2023)
- [Knowledge Verification to Nip Hallucination in the Bud](https://mlsanity.com/paper/2401.10768/) (2024)

## Built on by (most cited first, 30 of 43)

- [Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?](https://mlsanity.com/paper/2405.05904/) (2024)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition](https://mlsanity.com/paper/2505.22375/) (2025)
- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Scaling Laws for Fact Memorization of Large Language Models](https://mlsanity.com/paper/2406.15720/) (2024)
- [Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection](https://mlsanity.com/paper/2509.25502/) (2025)
- [Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization](https://mlsanity.com/paper/2405.09113/) (2024)
- [Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulation](https://mlsanity.com/paper/2503.08195/) (2025)
- [BianCang: A Traditional Chinese Medicine Large Language Model](https://mlsanity.com/paper/2411.11027/) (2024)
- [TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use](https://mlsanity.com/paper/2412.15495/) (2024)
- [GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation](https://mlsanity.com/paper/2502.05911/) (2025)
- [Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning](https://mlsanity.com/paper/2410.06913/) (2024)
- [DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification](https://mlsanity.com/paper/2504.13562/) (2025)
- [ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning](https://mlsanity.com/paper/2504.01400/) (2025)
- [Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs](https://mlsanity.com/paper/2601.08198/) (2026)
- [Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment](https://mlsanity.com/paper/2407.10804/) (2024)
- [Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels](https://mlsanity.com/paper/2509.16596/) (2025)
- [SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2512.07175/) (2025)
- [A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition](https://mlsanity.com/paper/2502.18702/) (2025)
- [Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey](https://mlsanity.com/paper/2505.03418/) (2025)
- [Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards](https://mlsanity.com/paper/2509.19003/) (2025)
- [Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation](https://mlsanity.com/paper/2509.26497/) (2025)
- [Focus On This, Not That! Steering LLMs with Adaptive Feature Specification](https://mlsanity.com/paper/2410.22944/) (2024)
- [KaFT: Knowledge-aware Fine-tuning for Boosting LLMs' Domain-specific Question-Answering Performance](https://mlsanity.com/paper/2505.15480/) (2025)
- [Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management](https://mlsanity.com/paper/2409.13191/) (2024)
- [60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering](https://mlsanity.com/paper/2409.15825/) (2024)
- [NILE: Internal Consistency Alignment in Large Language Models](https://mlsanity.com/paper/2412.16686/) (2024)
- [Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance](https://mlsanity.com/paper/2510.03528/) (2025)
- [A Layer-wise Analysis of Supervised Fine-Tuning](https://mlsanity.com/paper/2604.11838/) (2026)
- [Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs](https://mlsanity.com/paper/2408.09327/) (2024)

## Abstract (arXiv)

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works.