# LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization

arXiv: https://arxiv.org/abs/2311.17279 · HTML: https://mlsanity.com/paper/2311.17279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 28 Nov 2023.

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

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Reward (Mis)design for Autonomous Driving](https://mlsanity.com/paper/2104.13906/) (2021)
- [A Study of Checkpointing in Large Scale Training of Deep Neural Networks](https://mlsanity.com/paper/2012.00825/) (2020)
- [kDecay: Just adding k-decay items on Learning-Rate Schedule to improve Neural Networks](https://mlsanity.com/paper/2004.05909/) (2020)
- [What does fault tolerant Deep Learning need from MPI?](https://mlsanity.com/paper/1709.03316/) (2017)

## Built on by (most cited first, 1 of 1)

- [Interactive Training 2: Auditable Control Plane for Live Model Training](https://mlsanity.com/paper/2607.18314/) (2026)

## Abstract (arXiv)

Feedback-driven optimization, such as traditional machine learning training, is a static process that lacks real-time adaptability of hyperparameters. Tuning solutions for optimization require trial and error paired with checkpointing and schedulers, in many cases feedback from the algorithm is overlooked. Adjusting hyperparameters during optimization usually requires the program to be restarted, wasting utilization and time, while placing unnecessary strain on memory and processors. We present LiveTune, a novel framework allowing real-time parameter adjustment of optimization loops through LiveVariables. Live Variables allow for continuous feedback-driven optimization by storing parameters on designated ports on the system, allowing them to be dynamically adjusted. Extensive evaluations of our framework on standard machine learning training pipelines show saving up to 60 seconds and 5.4 Kilojoules of energy per hyperparameter change. We also show the feasibility and value of LiveTune in a reinforcement learning application where the users change the dynamics of the reward structure while the agent is learning showing 5x improvement over the baseline. Finally, we outline a fully automated workflow to provide end-to-end, unsupervised feedback-driven optimization.