# Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP

arXiv: https://arxiv.org/abs/1906.02768 · HTML: https://mlsanity.com/paper/1906.02768/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 30 of 73)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Composable Sparse Fine-Tuning for Cross-Lingual Transfer](https://mlsanity.com/paper/2110.07560/) (2021)
- [Learned Token Pruning for Transformers](https://mlsanity.com/paper/2107.00910/) (2021)
- [A Unified Lottery Ticket Hypothesis for Graph Neural Networks](https://mlsanity.com/paper/2102.06790/) (2021)
- [Modular Deep Learning](https://mlsanity.com/paper/2302.11529/) (2023)
- [Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization](https://mlsanity.com/paper/2105.12002/) (2021)
- [Advancing Model Pruning via Bi-level Optimization](https://mlsanity.com/paper/2210.04092/) (2022)
- [PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition](https://mlsanity.com/paper/2106.05933/) (2021)
- [Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?](https://mlsanity.com/paper/2107.00166/) (2021)
- [Dynamic Sparse Training for Deep Reinforcement Learning](https://mlsanity.com/paper/2106.04217/) (2021)
- [Playing Lottery Tickets with Vision and Language](https://mlsanity.com/paper/2104.11832/) (2021)
- [Efficient Lottery Ticket Finding: Less Data is More](https://mlsanity.com/paper/2106.03225/) (2021)
- [Recent Advances on Neural Network Pruning at Initialization](https://mlsanity.com/paper/2103.06460/) (2021)
- [In value-based deep reinforcement learning, a pruned network is a good network](https://mlsanity.com/paper/2402.12479/) (2024)
- [Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning](https://mlsanity.com/paper/2305.15065/) (2023)
- [GANs Can Play Lottery Tickets Too](https://mlsanity.com/paper/2106.00134/) (2021)
- [RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch](https://mlsanity.com/paper/2205.15043/) (2022)
- [XPrompt: Exploring the Extreme of Prompt Tuning](https://mlsanity.com/paper/2210.04457/) (2022)
- [Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks](https://mlsanity.com/paper/2110.14068/) (2021)
- [Discovering Language-neutral Sub-networks in Multilingual Language Models](https://mlsanity.com/paper/2205.12672/) (2022)
- [Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets](https://mlsanity.com/paper/2202.04736/) (2022)
- [Exploring Lottery Ticket Hypothesis in Spiking Neural Networks](https://mlsanity.com/paper/2207.01382/) (2022)
- [Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective](https://mlsanity.com/paper/2103.00397/) (2021)
- [On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning](https://mlsanity.com/paper/2105.01648/) (2021)
- [A Survey of Lottery Ticket Hypothesis](https://mlsanity.com/paper/2403.04861/) (2024)
- [Rethinking Graph Lottery Tickets: Graph Sparsity Matters](https://mlsanity.com/paper/2305.02190/) (2023)
- [Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks](https://mlsanity.com/paper/2206.01278/) (2022)
- [Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning](https://mlsanity.com/paper/2206.01342/) (2022)
- [You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership](https://mlsanity.com/paper/2111.00162/) (2021)
- [Robust Lottery Tickets for Pre-trained Language Models](https://mlsanity.com/paper/2211.03013/) (2022)

## Abstract (arXiv)

The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019). Intriguingly, this phenomenon suggests that initialization strategies for DNNs can be improved substantially, but the lottery ticket hypothesis has only previously been tested in the context of supervised learning for natural image tasks. Here, we evaluate whether "winning ticket" initializations exist in two different domains: natural language processing (NLP) and reinforcement learning (RL).For NLP, we examined both recurrent LSTM models and large-scale Transformer models (Vaswani et al., 2017). For RL, we analyzed a number of discrete-action space tasks, including both classic control and pixel control. Consistent with workin supervised image classification, we confirm that winning ticket initializations generally outperform parameter-matched random initializations, even at extreme pruning rates for both NLP and RL. Notably, we are able to find winning ticket initializations for Transformers which enable models one-third the size to achieve nearly equivalent performance. Together, these results suggest that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in DNNs.