GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models

2026-05-05 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

GIFT (Guided Fine-Tuning and Transfer) is a framework designed to enhance instruction-tuned language models by incorporating guidance from the instruction model during task adaptation. Existing methods often treat the instruction-tuned model as a passive target, only involved in the final merging stage. GIFT addresses this by fine-tuning a low-rank adapter on a pretrained base model, utilizing confidence signals derived from the instruction-tuned model. This guidance mechanism redistributes the learning signal, focusing optimization on regions consistent with the instruction model's alignment. The learned adapter is then merged into the instruction-tuned model, creating task-specialized models that retain general instruction-following behavior. Evaluations on mathematical and knowledge-intensive benchmarks, including Math500 and medical QA, show GIFT consistently outperforms direct fine-tuning and other transfer-based baselines, achieving an average accuracy improvement of 5.2% over Llama3.1-8B-Instruct on math tasks and a 6.2% gain on medical QA.

Key takeaway

For AI Engineers and Research Scientists adapting instruction-tuned LLMs to specific tasks, GIFT offers a principled approach to improve performance and stability. By leveraging the instruction model's confidence signals during base model adapter training, you can achieve superior task specialization without disrupting the model's general instruction-following capabilities. Consider integrating this guided fine-tuning strategy to enhance your model's accuracy on specialized benchmarks like mathematical reasoning or medical QA.

Key insights

GIFT enhances instruction-tuned models by guiding base model adapter training with instruction model confidence signals.

Principles

• Instruction models can actively guide task adaptation.
• Confidence signals from instruction models improve adapter quality.

Method

GIFT fine-tunes a low-rank adapter on a pretrained base model using confidence scores from the instruction-tuned model, then merges the adapter into the instruction model.

In practice

• Apply confidence-guided loss weighting for adapter training.
• Use low-rank adapters for efficient task-specific updates.

Topics

• GIFT Framework
• Instruction-Tuned LLMs
• Guided Fine-Tuning
• Low-Rank Adapters
• Model Merging

Code references

• sustech-nlp/gift

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• Build an LLM from Scratch 7: Instruction Finetuning — Instruction fine-tuning enables LLMs to follow diverse commands and generate free-form text responses.
• Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!! — RLHF aligns LLMs to human preferences by training a reward model from comparative feedback, then using it to fine-tune the LLM.
• References Improve LLM Alignment in Non-Verifiable Domains — Reference-guided LLM evaluators significantly improve model alignment and self-improvement in non-verifiable domains.
• No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages — Instruction transferring on pre-trained base models effectively specializes LLMs for no-resource languages without costly instruction fine-tuning.
• Making LLMs tell you how confident they really are through probe-targeted fine tuning.[R] — LLMs internally know their confidence but need fine-tuning to express it verbally, overcoming RLHF's bias.
• AI 101: Beyond RL: The New Fine-Tuning Stack for LLMs — Modern LLM fine-tuning is evolving into a modular stack, moving beyond monolithic RL to integrate diverse, efficient methods.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.