LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation

2026-04-14

AI Summary

This work addresses the challenge of distribution shift affecting machine learning models deployed on mobile devices in dynamic environments, where conventional online learning methods are often impractical due to high computational overhead. To overcome this limitation, the authors propose LightTune, a lightweight, backpropagation-free online fine-tuning framework that introduces, for the first time, a forward-only online update mechanism with theoretical convergence guarantees. LightTune employs a dynamic performance threshold to trigger fine-tuning only when necessary, leveraging test data to adapt the model while maintaining computational efficiency. Evaluated on block error rate (BLER) prediction in 6G systems, LightTune reduces prediction error by up to 48.8% and improves average throughput by 15.5% in link adaptation compared to the conventional OLLA algorithm.

Technology Category

Machine Learning: Online Learning & BanditsSearch and Optimization: Learning to SearchNatural Language Processing: Learning & Optimization for NLP

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time data only when performance falls below a predefined threshold, ensuring minimal computational overhead and highly efficient responsiveness. As a practical demonstration, we integrate LightTune into a block error rate (BLER) prediction algorithm for 6G mobile systems. This integration enables the ML BLER prediction model to dynamically adapt to previously unseen channel conditions in real-time. Our extensive results show a substantial reduction in the average BLER prediction error of up to 48.8% with online fine-tuning. Furthermore, we leverage this BLER prediction algorithm for link adaptation and demonstrate average throughput improvements of up to 15.5% compared to a conventional table-based outer loop link adaptation (OLLA) algorithm.

Problem

• online fine-tuning
• distributional shift
• resource-constrained devices
• mobile ML deployment
• continual adaptation

Innovation

• LightTune
• forward-only fine-tuning
• online adaptation
• lightweight learning
• link adaptation