Research map / Architectures and efficiency / Pruning and efficient tuning
Lottery ticket discovery
Finds trainable sparse subnetworks via iterative magnitude pruning and analyzes winning tickets.
42 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2019–2025) · an approach in Pruning and efficient tuning, keyword cluster “lottery · tickets · winning”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
9 papers in 2023–24 and 3 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 3.2% to 0.5%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2019 · The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (ICLR 2019 · 1,303 citations)
- 2020 · Rigging the Lottery: Making All Tickets Winners (ICML 2020 · 117 citations)
- 2021 · Winning Lottery Tickets in Deep Generative Models (AAAI 2021 · 25 citations)
- 2022 · Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win (AAAI 2022 · 37 citations)
- 2023 · Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis (CVPR 2023 · 12 citations)
- 2025 · Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry (ICML 2025 · 0 citations)
Earliest papers here
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (ICLR 2019 · 1,303 citations)
- Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask (NeurIPS 2019 · 105 citations)
- One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers (NeurIPS 2019 · 105 citations)
What has been tried
36 papers here report 411 measured changes (a component added, removed or swapped, compared with a baseline): helped 56%, no clear effect 13%, hurt 24%, mixed 7%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 12 papers; from OpenAlex and the papers' affiliations).
- University of Texas at Austin · 3 papers
- University of Texas, Austin · 2 papers
- Amazon · 2 papers
Use it
Download BibTeX of 23 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Pruning at initialization (24 papers)
Elsewhere in the topic
- Low-rank compression and merging (226 papers)
- Structured network pruning (162 papers)
- Adapter-based parameter-efficient tuning (149 papers)
- LoRA variants and theory (121 papers)
- Parameter-efficient transfer analysis (97 papers)
- Sparse training from scratch (95 papers)
- Layer-wise pruning of LLMs (91 papers)
- Joint pruning and quantization (78 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).