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Research map / Architectures and efficiency / Pruning and efficient tuning

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

ICLR 2020 · Architectures and efficiency › Pruning and efficient tuning · approach: Lottery ticket discovery · 97 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use winning-ticket initializations rather than random-ticket initializationsLSTM language model · Wikitext-2log perplexityHelped
Prune up to 90% of parameters using winning tickets with late rewindingLSTM language model · Wikitext-2log perplexity: 90%No clear effect
Remove late rewinding (ablation)LSTM language model · Wikitext-2log perplexityHurt
Replace iterative pruning with one-shot pruning (ablation)LSTM language model · Wikitext-2log perplexityHurt
Use winning-ticket rather than random-ticket initializationsTransformer · WMT'14 Newstest'14BLEUHelped
Add iterative pruning (ablation)Transformer · WMT'14 Newstest'14BLEUHelped
Add late rewinding (ablation)Transformer · WMT'14 Newstest'14BLEUHelped
Prune only Transformer layer weights while leaving embeddings intact (ablation)Transformer Base winning tickets · WMT'14 Newstest'14BLEUMixed
Train a 67%-pruned winning-ticket model from scratchTransformer Big · WMT'14 Newstest'14BLEU: 28.9 BLEU 67% pruned vs. 29.2 BLEU unprunedNo clear effect
Use winning-ticket rather than random-ticket initializationsClassic-control RL agents · CartPole-v0, Acrobot-v1, LunarLander-v2episodic rewardHelped
Use winning-ticket rather than random-ticket initializationsAtari RL agents · Atari gamesepisodic rewardMixed
Add late rewinding (ablation)RL agents · CartPole-v0, Acrobot-v1, LunarLander-v2, Assault, Breakout, Seaquestepisodic rewardHelped

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