RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment | Paper Citation Record

Observation 1d7f785c-3759-4d31-9acb-40d2261fd07e · outbound

This paper cites A definition of continual reinforcement learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment A definition of continual reinforcement learning

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This paper cites Vlmo: Unified vision-language pre-training with mixture-of-modality-experts.Advances in Neural Information Processing Systems, 35:32897–32912, 2022.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Vlmo: Unified vision-language pre-training with mixture-of-modality-experts.Advances in Neural Information Processing Systems, 35:32897–32912, 2022

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This paper cites Scail: Classifier weights scaling for class incremental learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Scail: Classifier weights scaling for class incremental learning

Reference 3

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This paper cites Efros, and Jun-Yan Zhu.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Efros, and Jun-Yan Zhu

Reference 4

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Observation 90b051d2-5db7-4ce2-ad59-50c25e11a50d · outbound

This paper cites Why Adversarial Training of ReLU Networks Is Difficult?.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Why Adversarial Training of ReLU Networks Is Difficult?

Reference 5

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Observation 1b6e7aa6-8f9d-48d4-84f4-53f2a5712c5d · outbound

This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023

Reference 6

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Observation c16b6d6a-24b8-4074-a4b7-d24cb1a8142b · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 7

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Observation 7acb0be7-da63-486d-9df8-03f2225a2a93 · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 8

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Observation 77277328-072e-4d85-8301-8a28d71ada6e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Imagenet: A large-scale hierarchical image database

Reference 9

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Observation 3bb93ac9-ea71-44f5-9840-7ad191d934e6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 883bdea7-2bd4-4b5b-989e-fd12c7deea70 · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Minimizing the accumulated trajectory error to improve dataset distillation

Reference 11

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RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 12

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Observation b7cfd9ae-34e6-4311-a17c-3eebeec68da6 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Sys- tems, 33:2881–2891, 2020.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment What neural networks memorize and why: Discovering the long tail via influence estimation.Advances in Neural Information Processing Sys- tems, 33:2881–2891, 2020

Reference 13

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Observation 56121782-6d0f-4254-904f-7700165bce50 · outbound

This paper cites Vissl.https://github.com/ facebookresearch/vissl, 2021.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Vissl.https://github.com/ facebookresearch/vissl, 2021

Reference 14

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RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

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Observation ab997ffa-0eb5-44f5-8504-5987b6ae4c61 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

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Observation 00da97ca-072f-481d-b10d-e8126f70e8a9 · outbound

This paper cites Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Reference 17

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Observation 4372fa9f-9b0d-4c6d-985d-017095c6ea10 · outbound

This paper cites Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Data-Efficient Training of CNNs and Transformers with Coresets: A Stability Perspective

Reference 18

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Observation a45dd16b-0dc1-4593-865c-b16441b02e4d · outbound

This paper cites Deep residual learning for image recognition.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Deep residual learning for image recognition

Reference 19

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Observation 75cee0b3-fa59-464e-8f69-0a3096d7eaea · outbound

This paper cites You only con- dense once: Two rules for pruning condensed datasets.Ad- vances in Neural Information Processing Systems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment You only con- dense once: Two rules for pruning condensed datasets.Ad- vances in Neural Information Processing Systems, 36, 2024

Reference 20

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Observation 758c6cb0-e95e-49a0-a04c-9a384d419269 · outbound

This paper cites The many faces of robust- ness: A critical analysis of out-of-distribution generalization.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment The many faces of robust- ness: A critical analysis of out-of-distribution generalization

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Observation cb460f3d-3f95-4274-98d1-1d4762850458 · outbound

This paper cites Natural adversarial examples.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Natural adversarial examples

Reference 22

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Observation 5aaafcea-702e-4245-b2cd-9d085bb8b35b · outbound

This paper cites Diversified Batch Selection for Training Acceleration.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Diversified Batch Selection for Training Acceleration

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Observation f74a5973-2481-495c-b184-2d85b3b3ed80 · outbound

This paper cites DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning

Reference 24

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Observation 657989d9-7467-4baa-bead-07fc94138d60 · outbound

This paper cites Densely connected convolutional net- works.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Densely connected convolutional net- works

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Observation 24a607f5-fe57-4462-9564-e4e4108a5ebc · outbound

This paper cites Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks.Journal of Computer and System Sciences, 54(1): 169–176, 1997.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks.Journal of Computer and System Sciences, 54(1): 169–176, 1997

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Observation cfe58b0c-0de6-4e3c-8baa-85619fe30366 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Grad-match: Gradient matching based data subset selection for efficient deep model training

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This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Glister: Generalization based data subset selection for efficient and robust learning

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This paper cites Segment any- thing.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Segment any- thing

Reference 29

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Observation 6361aad5-7521-40bc-aba3-94f496a32bac · outbound

This paper cites Understanding black-box predictions via influence functions.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Understanding black-box predictions via influence functions

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Observation 027efbde-5693-4bbe-8cef-66addf2be9d3 · outbound

This paper cites Understanding black-box predictions via influence functions.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Understanding black-box predictions via influence functions

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This paper cites Prism: A unified framework of parameterized submodular information measures for tar- geted data subset selection and summarization.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Prism: A unified framework of parameterized submodular information measures for tar- geted data subset selection and summarization

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Observation 3f08a32b-6ecc-445f-8cfb-597745813083 · outbound

This paper cites Learning multiple layers of features from tiny images.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Learning multiple layers of features from tiny images

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This paper cites DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization

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Observation e95a7e29-9f31-4945-937b-a82bdbba3ad7 · outbound

This paper cites Super- vised pretraining can learn in-context reinforcement learn- ing.Advances in Neural Information Processing Systems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Super- vised pretraining can learn in-context reinforcement learn- ing.Advances in Neural Information Processing Systems, 36, 2024

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Observation 71d5c2c3-5c70-4f06-b344-e789934f35be · outbound

This paper cites A Comprehensive Survey of Dataset Distillation.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment A Comprehensive Survey of Dataset Distillation

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This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021

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This paper cites Design from policies: Con- servative test-time adaptation for offline policy optimiza- tion.Advances in Neural Information Processing Systems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Design from policies: Con- servative test-time adaptation for offline policy optimiza- tion.Advances in Neural Information Processing Systems, 36, 2024

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Observation 3d975c5f-eeed-406c-b786-e40d10cbe653 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Swin transformer: Hierarchical vision transformer using shifted windows

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This paper cites Struc- tured state space models for in-context reinforcement learn- ing.Advances in Neural Information Processing Systems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Struc- tured state space models for in-context reinforcement learn- ing.Advances in Neural Information Processing Systems, 36, 2024

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Observation 8942c9be-2da6-45e4-a55c-f84ed0cb586e · outbound

This paper cites D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning

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RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Language Models are Few-Shot Learners

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This paper cites Schulze Buschoff, Robert Geirhos, and Felix A.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Schulze Buschoff, Robert Geirhos, and Felix A

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Observation b9bdb6ca-15f7-4e74-9892-5258f98e847a · outbound

This paper cites Coresets for data-efficient training of machine learning mod- els.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Coresets for data-efficient training of machine learning mod- els

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Observation 433b45d1-7f48-4c61-9f21-ec257892d076 · outbound

This paper cites Asynchronous Methods for Deep Reinforcement Learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Asynchronous Methods for Deep Reinforcement Learning

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This paper cites Asynchronous methods for deep reinforcement learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Asynchronous methods for deep reinforcement learning

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Observation 5db15f97-96d7-42bc-b595-154cb956d87d · outbound

This paper cites Performance bounds for policy-based average reward rein- forcement learning algorithms.Advances in Neural Infor- mation Processing Systems, 36:19386–19396, 2023.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Performance bounds for policy-based average reward rein- forcement learning algorithms.Advances in Neural Infor- mation Processing Systems, 36:19386–19396, 2023

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Observation 53a30719-06d6-444b-b452-d3f4f845354e · outbound

This paper cites Bridging the gap between value and policy based reinforcement learning.Advances in neural informa- tion processing systems, 30, 2017.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Bridging the gap between value and policy based reinforcement learning.Advances in neural informa- tion processing systems, 30, 2017

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Observation 7332f4e7-e5a5-4371-950c-1cf62d23174a · outbound

This paper cites Data valu- ation without training of a model.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Data valu- ation without training of a model

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Observation de7d3119-ef20-4115-8fe5-e06648aa3555 · outbound

This paper cites Deep learning on a data diet: Finding important ex- amples early in training.Advances in Neural Information Processing Systems, 34:20596–20607, 2021.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Deep learning on a data diet: Finding important ex- amples early in training.Advances in Neural Information Processing Systems, 34:20596–20607, 2021

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Observation 6b7999bb-6075-49b1-9489-0bf17619a88e · outbound

This paper cites Adaptive second order coresets for data-efficient machine learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Adaptive second order coresets for data-efficient machine learning

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Observation c76ea08c-374c-4588-99e3-c699b1e0358b · outbound

This paper cites InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

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Observation 4799d44e-16e9-4864-adcc-e0d98b7009ab · outbound

This paper cites Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019

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Observation 36e64bc2-ccbb-4ebb-9565-dbaabd9708c3 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Learning transferable visual models from natural language supervi- sion

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Observation 8449f8d1-ff33-410a-acd7-1fb22db86278 · outbound

This paper cites Accelerating Deep Learning with Dynamic Data Pruning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Accelerating Deep Learning with Dynamic Data Pruning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:59.959130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96691100-8007-47ae-8b62-b01251832e72 · outbound

This paper cites Data-centric green artifi- cial intelligence: A survey.IEEE Transactions on Artificial Intelligence, 2023.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Data-centric green artifi- cial intelligence: A survey.IEEE Transactions on Artificial Intelligence, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.614490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1bbc9bc1-0050-4607-a756-861674ac5a4c · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- formation Processing Systems, 35:25278–25294, 2022.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural In- formation Processing Systems, 35:25278–25294, 2022

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.599369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad0889fd-ee4b-4245-8c59-13991499750b · outbound

This paper cites Active learning for convolu- tional neural networks: A core-set approach.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Active learning for convolu- tional neural networks: A core-set approach

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.584354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b8662fc-5539-4464-95fb-0b83368c6491 · outbound

This paper cites Reinforcement learning algorithms: A brief survey.Expert Systems with Applications, page 120495, 2023.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Reinforcement learning algorithms: A brief survey.Expert Systems with Applications, page 120495, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.570745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7336274-2974-49be-9562-49f9f4d561c7 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:59.982305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f496b4d-f4ee-4d8c-ab72-f6c824317b1f · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.556595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e3fd2e8a-14e9-42b2-b66b-da13d20d7415 · outbound

This paper cites On the depth of deep neural networks: A the- oretical view.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment On the depth of deep neural networks: A the- oretical view

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.542933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ff49d75-f427-41a4-af03-6a1ad8ec5c5b · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.529153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3a94ca87-b5cc-4c21-9da3-af85ad86911a · outbound

This paper cites Data pruning via moving-one- sample-out.Advances in Neural Information Processing Sys- tems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Data pruning via moving-one- sample-out.Advances in Neural Information Processing Sys- tems, 36, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.514192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 59fd0781-47d3-47ed-9055-61bae4ab7299 · outbound

This paper cites D4: Improving llm pretraining via document de- duplication and diversification.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment D4: Improving llm pretraining via document de- duplication and diversification

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.499133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2049c0e2-7e19-4698-b443-a87f6c19fd46 · outbound

This paper cites Policy-based reinforcement learning for generalisation in interactive text- based environments.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Policy-based reinforcement learning for generalisation in interactive text- based environments

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.484792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c466a90-d7ec-4953-9755-3964be828308 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:00.018900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5443d804-1ce6-4aab-aa1a-db7ac94fa21a · outbound

This paper cites Visualizing data using t-sne.J.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Visualizing data using t-sne.J

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.469928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4df00de2-d421-4c1d-9a53-b6797adfb7d9 · outbound

This paper cites Submodularity in data subset selection and active learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Submodularity in data subset selection and active learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.455132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ff3b0b43-e527-40a8-a322-cd3cdc381a0d · outbound

This paper cites Herding dynamical weights to learn.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Herding dynamical weights to learn

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.439843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6c4e3a4a-3eb1-4758-bfb5-47a86b26cbe6 · outbound

This paper cites Scalable trust-region method for deep re- inforcement learning using kronecker-factored approxima- tion.Advances in neural information processing systems, 30, 2017.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Scalable trust-region method for deep re- inforcement learning using kronecker-factored approxima- tion.Advances in neural information processing systems, 30, 2017

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.424377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 42a13abe-93a8-496e-9ad1-85868addd374 · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.410694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 467d9d2f-3356-4177-9f05-2575f5c15d47 · outbound

This paper cites Dataset pruning: Reducing training data by ex- amining generalization influence.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Dataset pruning: Reducing training data by ex- amining generalization influence

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.394794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5d31db3-065d-4aa1-8875-1f47896ec3e3 · outbound

This paper cites Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:00.186939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c697fb3-1057-4099-bd62-229d5b9b0e16 · outbound

This paper cites In- vestigating the effectiveness of data augmentation from simi- larity and diversity: An empirical study.Pattern Recognition, 148:110204, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment In- vestigating the effectiveness of data augmentation from simi- larity and diversity: An empirical study.Pattern Recognition, 148:110204, 2024

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.380754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f6263b7d-935d-4b9d-9425-85aafa11aa6b · outbound

This paper cites AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.721993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0fdadc98-eae9-4b48-8e1e-81d6ade9aafb · outbound

This paper cites Entaugment: Entropy-driven adaptive data augmentation framework for image classification.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Entaugment: Entropy-driven adaptive data augmentation framework for image classification

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.366373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f36bed3d-fe36-4a96-871a-01cc7d39aa1f · outbound

This paper cites A CLIP-Powered Framework for Robust and Generalizable Data Selection.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment A CLIP-Powered Framework for Robust and Generalizable Data Selection

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:00.463710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41f0be0b-0fc1-4466-a89b-a3e2d06bb2af · outbound

This paper cites When Dynamic Data Selection Meets Data Augmentation.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment When Dynamic Data Selection Meets Data Augmentation

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:00.502202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc560aa-b306-44a1-aaa7-201bd79e6472 · outbound

This paper cites Nearly op- timal vc-dimension and pseudo-dimension bounds for deep neural network derivatives.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Nearly op- timal vc-dimension and pseudo-dimension bounds for deep neural network derivatives

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.350957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5cd0c3f-029c-4e47-adb5-8f3e4aebfd35 · outbound

This paper cites $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:46:01.668249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aca3617e-a97a-45b6-886c-74f64ea7d918 · outbound

This paper cites Metalight: Value-based meta-reinforcement learning for traffic signal control.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Metalight: Value-based meta-reinforcement learning for traffic signal control

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.335805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc4e0b73-f622-47e3-939c-1c9a84a9cb2a · outbound

This paper cites Accelerating dataset distillation via model augmenta- tion.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Accelerating dataset distillation via model augmenta- tion

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.320800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5145a703-5fd9-4d7a-8613-83f7890a8d59 · outbound

This paper cites Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.306102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e7412f3e-1c85-4513-b0bb-8fc8d7a372bb · outbound

This paper cites Selectivity drives productivity: Efficient dataset prun- ing for enhanced transfer learning.Advances in Neural In- formation Processing Systems, 36, 2024.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Selectivity drives productivity: Efficient dataset prun- ing for enhanced transfer learning.Advances in Neural In- formation Processing Systems, 36, 2024

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.291133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb954907-9de4-4d40-90ed-5e4da4bd7c72 · outbound

This paper cites Coverage-centric coreset selection for high pruning rates.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Coverage-centric coreset selection for high pruning rates

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.276071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b504299d-eaed-4d2c-92f2-d05f8b63170b · outbound

This paper cites Dataset quantization.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Dataset quantization

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.259087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 49af3629-c284-4a2e-a2c6-37542003e6c9 · outbound

This paper cites Dataset Distillation using Neural Feature Regression.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Dataset Distillation using Neural Feature Regression

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:01.357184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 820bb129-b97c-4b0f-b838-41a6905dd2c7 · outbound

This paper cites ˆyi − ˆyj =∥w(x i −x j)∥(12) ≤ ∥w∥ ∥xi −x j∥(13) ≤ϵ∥w∥(14) (15) In the above, the Inequality(13)follows from H ¨older’s inequality.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment ˆyi − ˆyj =∥w(x i −x j)∥(12) ≤ ∥w∥ ∥xi −x j∥(13) ≤ϵ∥w∥(14) (15) In the above, the Inequality(13)follows from H ¨older’s inequality

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.244079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4cc18842-905c-412c-868b-ff0323942e68 · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.227674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 912c5b4c-e5e8-4a9c-88f4-eb8de8e36629 · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.212016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7eeebbd-b433-478c-b360-1d38c72ba471 · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.197233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8cbbd3d0-0eef-4a09-aa17-db939f8d86d9 · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.181469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5428e772-4ce3-474e-88d1-34e11d76d88f · outbound

This paper cites The total epoch is 200, and no warm-up schedule is used.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment The total epoch is 200, and no warm-up schedule is used

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.166571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7741c6a8-88b1-44d4-b969-cd21e9e76784 · outbound

This paper cites The A2C network architecture details.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment The A2C network architecture details

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.151473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0aefc2e8-56c9-441a-b2e8-bed2d33e4e1e · outbound

This paper cites The complexities of the first two steps areO N 2 k d andO N 2 k , respectively, whereN k is the number of sam- ples in classkanddis the feature dimension (e.g., 512 for ResNet-18).

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment The complexities of the first two steps areO N 2 k d andO N 2 k , respectively, whereN k is the number of sam- ples in classkanddis the feature dimension (e.g., 512 for ResNet-18)

Reference 96

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:46:02.136574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7417447-d8ff-4ca7-a844-ce983a66109c · outbound

This paper cites an unresolved cited work.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:46:02.120060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5748ba80-4ad1-4be4-b1a5-a8716854be97 · outbound

This paper cites For experiments on Tiny-ImageNet, following [72], we adopt a batch size of 256, an SGD opti- mizer with a momentum of 0.9, weight decay of 1e-4, and an initial learning rate of 0.1.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment For experiments on Tiny-ImageNet, following [72], we adopt a batch size of 256, an SGD opti- mizer with a momentum of 0.9, weight decay of 1e-4, and an initial learning rate of 0.1

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.104613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6b66a7a-8fa4-44f5-87d9-7117fd9f9489 · outbound

This paper cites Specifically, once the selected datasets are obtained, only a subset needs to be stored as a replacement for the full dataset, leading to savings in memory costs.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment Specifically, once the selected datasets are obtained, only a subset needs to be stored as a replacement for the full dataset, leading to savings in memory costs

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.088715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a4e3880-05cd-4cad-83e5-7cff299da327 · outbound

This paper cites As shown in Table 8, our method can achieve superior results.

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment As shown in Table 8, our method can achieve superior results

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:46:02.072710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers