# Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation

arXiv: https://arxiv.org/abs/2605.25170 · HTML: https://mlsanity.com/paper/2605.25170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent dynamics models and model-based RL, one of 11 directions in World models.
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 24 May 2026.

## Placed in

- World models / [Latent dynamics models and model-based RL](https://mlsanity.com/wm/latent-dynamics-models-and-model-based-rl/)

## Builds on (most cited first, 9 of 9)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Olmo 3](https://mlsanity.com/paper/2512.13961/) (2025)
- [Architecture Matters in Continual Learning](https://mlsanity.com/paper/2202.00275/) (2022)
- [Less is More: Recursive Reasoning with Tiny Networks](https://mlsanity.com/paper/2510.04871/) (2025)
- [Generative Recursive Reasoning](https://mlsanity.com/paper/2605.19376/) (2026)
- [Neural Plasticity Networks](https://mlsanity.com/paper/1908.08118/) (2019)
- [Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.11706/) (2025)

## Abstract (arXiv)

Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models. Olfactory navigation is a highly dynamic and non-stationary task that benefits from real-time continual learning. We introduce an adaptive framework called Grow-Prune-Freeze (GPF) networks that enable an agent to continually learn through growing, pruning, and freezing early layers of its policy in response to world complexity. Grounding GPFs in non-linear random matrix theory, we show that the work of Pennington & Worth (2017) can be extended from single hidden layers to n-layer continual-learning models, and that eigenvalue composition of network weights is preserved as successive layers are added. We show that GPFs based on Expected SARSA achieve a 94% success rate on turbulent plume navigation - a partially observable, non-stationary task representative of the "big world" challenges that motivate adaptive learning in robotics - and provide supporting methodology for applying GPFs in other world models. Further experiments amount evidence that GPFs may generalize well to other machine learning tasks such as reinforcement learning in Atari, image classification, and autoregressive language models. We open source all code and data to encourage improvements on and more research in olfactory robotics.