---
title: "Who cites Progress & Compress: A scalable — 66 citing papers"
description: "66 papers of the Elamur Research library cite «Progress & Compress: A scalable framework for continual learning», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Progress & Compress: A scalable framework for…](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/) / cited by

# Who cites «Progress & Compress: A scalable framework for continual learning»

66 papers of our library cite it (of the 149,439 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «Progress & Compress: A scalable framework for continual learning»](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/)

1. **[Parameter-Efficient Transfer Learning with Diff Pruning](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Demi Guo, Alexander Rush, Yoon Kim*2021 · 367 citations*
2. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yong Lin, Hangyu Lin, Wei Xiong, et al.*2024 · 83 citations*
3. **[Sparse Adapter Fusion for Continual Learning in NLP](https://md.research.elamur.ai/free/en/papers/zeng-2026-sparse-adapter-fusion-continual-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Min Zeng, Xi Chen, Haiqin Yang, et al.*2026 · 1 citation*
4. **[Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning](https://md.research.elamur.ai/free/en/papers/wu-2026-agent-dice-disentangling-knowledge-updates/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Wu, Xingyu Lou, Xinbei Ma, et al.*2026*

62 more papers of our library cite it; they have no page here yet.

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