# WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation

arXiv: https://arxiv.org/abs/2603.17301 · HTML: https://mlsanity.com/paper/2603.17301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 38 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 30 of 31)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation](https://mlsanity.com/paper/2106.04399/) (2021)
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Trajectory balance: Improved credit assignment in GFlowNets](https://mlsanity.com/paper/2201.13259/) (2022)
- [Biological Sequence Design with GFlowNets](https://mlsanity.com/paper/2203.04115/) (2022)
- [GFlowNets and variational inference](https://mlsanity.com/paper/2210.00580/) (2022)
- [A theory of continuous generative flow networks](https://mlsanity.com/paper/2301.12594/) (2023)
- [Better Training of GFlowNets with Local Credit and Incomplete Trajectories](https://mlsanity.com/paper/2302.01687/) (2023)
- [Towards Understanding and Improving GFlowNet Training](https://mlsanity.com/paper/2305.07170/) (2023)
- [Robust Scheduling with GFlowNets](https://mlsanity.com/paper/2302.05446/) (2023)
- [Generative Augmented Flow Networks](https://mlsanity.com/paper/2210.03308/) (2022)
- [Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network](https://mlsanity.com/paper/2305.19366/) (2023)
- [Dynamic Sparse Training for Deep Reinforcement Learning](https://mlsanity.com/paper/2106.04217/) (2021)
- [Stochastic Generative Flow Networks](https://mlsanity.com/paper/2302.09465/) (2023)
- [CFlowNets: Continuous Control with Generative Flow Networks](https://mlsanity.com/paper/2303.02430/) (2023)
- [Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity](https://mlsanity.com/paper/2404.03570/) (2024)
- [Understanding and Mitigating the Limitations of Prioritized Experience Replay](https://mlsanity.com/paper/2007.09569/) (2020)
- [Accuracy-based Curriculum Learning in Deep Reinforcement Learning](https://mlsanity.com/paper/1806.09614/) (2018)
- [A Theory of Non-Acyclic Generative Flow Networks](https://mlsanity.com/paper/2312.15246/) (2023)
- [Provably Safe Reinforcement Learning with Step-wise Violation Constraints](https://mlsanity.com/paper/2302.06064/) (2023)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance](https://mlsanity.com/paper/2112.03575/) (2021)
- [Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment](https://mlsanity.com/paper/2311.01059/) (2023)
- [Expected flow networks in stochastic environments and two-player zero-sum games](https://mlsanity.com/paper/2310.02779/) (2023)
- [Concurrent Meta Reinforcement Learning](https://mlsanity.com/paper/1903.02710/) (2019)
- [Fault-Aware Robust Control via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2011.08728/) (2020)
- [GFlowNet Training by Policy Gradients](https://mlsanity.com/paper/2408.05885/) (2024)
- [Streaming Bayes GFlowNets](https://mlsanity.com/paper/2411.05899/) (2024)
- [Multi-Agent Continuous Control with Generative Flow Networks](https://mlsanity.com/paper/2408.06920/) (2024)
- [Complementary Meta-Reinforcement Learning for Fault-Adaptive Control](https://mlsanity.com/paper/2009.12634/) (2020)

## Abstract (arXiv)

Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper introduces WINFlowNets, a novel CFlowNets framework that enables the co-training of flow and retrieval networks. WINFlowNets begins with a warm-up phase for the retrieval network to bootstrap its policy, followed by a shared training architecture and a shared replay buffer for co-training both networks. Experiments in simulated robotic environments demonstrate that WINFlowNets surpasses CFlowNets and state-of-the-art RL algorithms in terms of average reward and training stability. Furthermore, WINFlowNets exhibits strong adaptive capability in fault environments, making it suitable for tasks that demand quick adaptation with limited sample data. These findings highlight WINFlowNets' potential for deployment in dynamic and malfunction-prone robotic systems, where traditional pre-training or sample inefficient data collection may be impractical.