Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:43:02.115828Z
Paper Citation Record · LEDGER
MARS: Unleashing the Power of Variance Reduction for Training Large Models
As of 23 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 20 inbound Pith citation observations for arXiv:2411.10438.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:20:51.021889Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:18:56.218978Z
Reference resolution
95 of 95 outbound references displayed
- verified exact2
- verified fuzzy23
- unresolved70
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- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 32587236-7371-4bc6-8cae-c4c9317b1f20 · outbound
This paper cites and Yuan, Y.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Yuan, Y
Reference 1
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c23494a3-6110-4f42-b1fb-ef0b3e02ddac · outbound
This paper cites Scalable Second Order Optimization for Deep Learning.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Scalable Second Order Optimization for Deep Learning
Reference 2
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 489e2f04-193e-4726-9e4f-70c67a967204 · outbound
This paper cites C., Foster, D.
MARS: Unleashing the Power of Variance Reduction for Training Large Models C., Foster, D
Reference 3
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3cade9e-05c8-4b36-a45c-aa930683b285 · outbound
This paper cites and Glynn, P.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Glynn, P
Reference 4
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 75163b56-d8a4-4c74-b8e2-2ff5c75ab8cd · outbound
This paper cites Old Optimizer, New Norm: An Anthology.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Old Optimizer, New Norm: An Anthology
Reference 5
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34e7797b-eb8b-4859-a7d8-4b4e61f5733d · outbound
This paper cites L., Gao, J., and Choi, Y.
MARS: Unleashing the Power of Variance Reduction for Training Large Models L., Gao, J., and Choi, Y
Reference 6
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0af577b4-de4b-447b-a9c0-132775e4e360 · outbound
This paper cites Language Models are Few-Shot Learners.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Language Models are Few-Shot Learners
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18cd5d9d-fd4e-4d79-9728-7799a3146168 · outbound
This paper cites Stochastic spectral descent for restricted boltzmann machines.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Stochastic spectral descent for restricted boltzmann machines
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f271a367-fd5f-4c90-be6b-be2c2a5d4c4f · outbound
This paper cites Stochastic spectral descent for discrete graphical models.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Stochastic spectral descent for discrete graphical models
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a327634-7f86-4bdd-abcb-41771812372d · outbound
This paper cites Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5a69e144-f8f0-4d1e-a106-01c139073854 · outbound
This paper cites Symbolic discovery of optimization algorithms.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Symbolic discovery of optimization algorithms
Reference 11
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de0da958-9185-4240-ab0b-e12a0a20c986 · outbound
This paper cites W., Sutton, C., Gehrmann, S., et al.
MARS: Unleashing the Power of Variance Reduction for Training Large Models W., Sutton, C., Gehrmann, S., et al
Reference 12
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4dfdbe6d-757e-4d09-aded-a15b2248b3ce · outbound
This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Boolq: Exploring the surprising difficulty of natural yes/no questions
Reference 13
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6986463-4542-4b2e-9862-06881e5d84fe · outbound
This paper cites and Orabona, F.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Orabona, F
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11856636-9353-4a77-a17b-83886cbbd8a4 · outbound
This paper cites and Bottou, L.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Bottou, L
Reference 15
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 383cc93d-6e19-4562-b8f7-bf25784f1e2f · outbound
This paper cites Saga: A fast incremental gradient method with support for non-strongly convex composite objectives.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Reference 16
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 473f2f71-6b58-4f5c-baaf-687600880774 · outbound
This paper cites The Road Less Scheduled.
MARS: Unleashing the Power of Variance Reduction for Training Large Models The Road Less Scheduled
Reference 17
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 311715d3-4e4b-4421-a965-f5f1ccda8f22 · outbound
This paper cites Stochastic variance-reduced newton: Accelerating finite-sum minimization with large batches.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Stochastic variance-reduced newton: Accelerating finite-sum minimization with large batches
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9ce96ff-9c55-4f14-b1e6-4d48be2963c5 · outbound
This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
MARS: Unleashing the Power of Variance Reduction for Training Large Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c084cd7b-690e-4278-8d2c-dd4f5fcfbf75 · outbound
This paper cites The Llama 3 Herd of Models.
MARS: Unleashing the Power of Variance Reduction for Training Large Models The Llama 3 Herd of Models
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82f877d8-2988-4dd1-9e2e-4e596f507325 · outbound
This paper cites Adaptive subgradient methods for online learning and stochastic optimization.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Adaptive subgradient methods for online learning and stochastic optimization
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7450097-b671-4ddc-96e0-bb465215a037 · outbound
This paper cites J., Lin, Z., and Zhang, T.
MARS: Unleashing the Power of Variance Reduction for Training Large Models J., Lin, Z., and Zhang, T
Reference 22
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2867a9b-db15-4931-a3b8-dbaa05ed9173 · outbound
This paper cites Promise: Preconditioned stochastic optimization methods by incorporating scalable curvature estimates.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Promise: Preconditioned stochastic optimization methods by incorporating scalable curvature estimates
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 28f611b6-5a1a-40d5-8130-e98ebebb5e23 · outbound
This paper cites A framework for few-shot language model evaluation, 07 2024.
MARS: Unleashing the Power of Variance Reduction for Training Large Models A framework for few-shot language model evaluation, 07 2024
Reference 24
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 332c3eeb-a786-460f-91d7-5586729a2c81 · outbound
This paper cites and Lan, G.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Lan, G
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a9c5b38-f97a-4ac3-ad29-1b5a50e3f11e · outbound
This paper cites Openwebtext corpus.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Openwebtext corpus
Reference 26
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0090491-0933-4f80-86d8-14c98d7ad1e8 · outbound
This paper cites and Graves, A.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Graves, A
Reference 27
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c99be9de-a6d6-4e29-ab8a-075002b83bb4 · outbound
This paper cites Shampoo: Preconditioned stochastic tensor optimization.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Shampoo: Preconditioned stochastic tensor optimization
Reference 28
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 837873a5-4868-487b-9676-6051d1199ade · outbound
This paper cites Beyond convexity: Stochastic quasi-convex optimization.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Beyond convexity: Stochastic quasi-convex optimization
Reference 29
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0eb16ae7-421a-4793-818f-b9012f330f17 · outbound
This paper cites Deep residual learning for image recognition.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Deep residual learning for image recognition
Reference 30
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 365be778-5806-4500-81d9-2650b84c20ee · outbound
This paper cites Measuring massive multitask language understanding.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Measuring massive multitask language understanding
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 7c0c1a4f-42c9-4135-80fe-d10eab8c40bc · outbound
This paper cites an unresolved cited work.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation a44fc610-5fee-424c-903f-eeff8f848111 · outbound
This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.
MARS: Unleashing the Power of Variance Reduction for Training Large Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Reference 33
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f860c1b5-000b-43e3-90d6-ad060a880bd7 · outbound
This paper cites Super-adam: faster and universal framework of adaptive gradients.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Super-adam: faster and universal framework of adaptive gradients
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 095ffd0c-484f-441a-958f-0ccf4ac6f5a4 · outbound
This paper cites and Zhang, T.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Zhang, T
Reference 35
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6ac791e-5db5-4467-b101-9f19dc00ae43 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Muon: An optimizer for hidden layers in neural networks, 2024
Reference 36
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22458ebd-08da-48ea-9dec-10abb76e7a98 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 37
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Source-reported events for the cited work
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Observation d99fb412-a9cf-421e-93ec-20e084cb9f06 · outbound
This paper cites an unresolved cited work.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 38
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Source-reported events for the cited work
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Observation 7db134d0-4a1a-44a0-8ce9-914866c1da56 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models T., and Cevher, V
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e0978da1-f323-430f-9617-c1a31cc3e799 · outbound
This paper cites an unresolved cited work.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 40
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 579e6a5e-ef91-48af-ae00-7b31d2e79891 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Learning multiple layers of features from tiny images
Reference 41
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cce1d6db-0167-4dcb-b47f-59bb14ca9253 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models On the computation of the matrix k-th root
Reference 42
- Resolution
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Source-reported events for the cited work
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Observation 6b6aa78d-cce2-4b06-9429-5f6a6637dc43 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models S., Moeller, T
Reference 43
- Resolution
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Source-reported events for the cited work
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Observation 564e5370-2bc4-40a4-bf8c-75b95e12b00b · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Gradient-based learning applied to document recognition
Reference 44
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29b4ed67-9353-458f-8251-93feac329703 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Storm+: Fully adaptive sgd with recursive momentum for nonconvex optimization
Reference 45
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 49389a72-448f-4e61-b719-5bd1f2b428dc · outbound
This paper cites Smoothness and Adaptivity in Nonlinear Optimization for Machine Learning Applications.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Smoothness and Adaptivity in Nonlinear Optimization for Machine Learning Applications
Reference 46
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 28f3b14e-136d-4d1c-a3ae-94871d9d4cae · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Convergence of adam under relaxed assumptions
Reference 47
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 476a60c7-8422-4d96-af61-7dabfc07b7ce · outbound
This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.
MARS: Unleashing the Power of Variance Reduction for Training Large Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Reference 48
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6704f74b-430b-4ac6-aabf-bd407257fa08 · outbound
This paper cites Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
Reference 49
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 929c6321-4e29-4615-9ed1-0bff2eb4f95e · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Adam$^+$: A Stochastic Method with Adaptive Variance Reduction
Reference 50
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98bfc228-49f1-4c1d-90ea-7fcf22fbdc3f · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models and Hutter, F
Reference 51
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7feaa9b5-925b-4996-a482-6bb4a927d258 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Fineweb-edu: the finest collection of educational content, 2024
Reference 52
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0d21d3e8-2f8b-4f1a-b690-c0ab786f0e98 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models and Grosse, R
Reference 53
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7297947-5172-401e-93a6-a3807bcf4d5b · outbound
This paper cites and Tropp, J.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Tropp, J
Reference 54
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c3cd4b70-9543-4b57-8d1f-e0488227881d · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models An Empirical Model of Large-Batch Training
Reference 55
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2c25452-1b46-4637-9aff-884ce3bff0e5 · outbound
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MARS: Unleashing the Power of Variance Reduction for Training Large Models Adaptive Bound Optimization for Online Convex Optimization
Reference 56
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68ea52d1-0b27-4be9-be39-b1dd82794446 · outbound
This paper cites Can a suit of armor conduct electricity? A new dataset for open book question answering.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Can a suit of armor conduct electricity? A new dataset for open book question answering
Reference 57
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af18d8a0-4378-440b-9257-b35ebf83d8c2 · outbound
This paper cites A New Perspective on Shampoo's Preconditioner.
MARS: Unleashing the Power of Variance Reduction for Training Large Models A New Perspective on Shampoo's Preconditioner
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.891022Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85cfd310-4490-42d3-a135-aa6de65968f7 · outbound
This paper cites A method for solving the convex programming problem with convergence rate o(1/k^2).
MARS: Unleashing the Power of Variance Reduction for Training Large Models A method for solving the convex programming problem with convergence rate o(1/k^2)
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.898101Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2845d882-48fc-4566-8ce3-ed4e2923103f · outbound
This paper cites Introductory lectures on convex optimization: A basic course, volume 87.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Introductory lectures on convex optimization: A basic course, volume 87
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.410000Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4ff75ab9-714f-4a67-b7f3-503bb0df5637 · outbound
This paper cites M., Liu, J., Scheinberg, K., and Tak \'a c , M.
MARS: Unleashing the Power of Variance Reduction for Training Large Models M., Liu, J., Scheinberg, K., and Tak \'a c , M
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.379053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3f2a9ec6-1ccb-468c-bb19-2d2e95021808 · outbound
This paper cites Stochastic Recursive Gradient Algorithm for Nonconvex Optimization.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Stochastic Recursive Gradient Algorithm for Nonconvex Optimization
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.913923Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16aaf30a-2629-4f06-990b-25a4de6a3f11 · outbound
This paper cites Language models are unsupervised multitask learners.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Language models are unsupervised multitask learners
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.919275Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28c67352-ff2d-457b-b4a0-f5a51d15074e · outbound
This paper cites J., Hefny, A., Sra, S., Poczos, B., and Smola, A.
MARS: Unleashing the Power of Variance Reduction for Training Large Models J., Hefny, A., Sra, S., Poczos, B., and Smola, A
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.332688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 64e703b0-6003-4ae7-ac02-cbd442a56063 · outbound
This paper cites On the Convergence of Adam and Beyond.
MARS: Unleashing the Power of Variance Reduction for Training Large Models On the Convergence of Adam and Beyond
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.936920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43854652-eacd-442b-9a6d-4ecb8e5021ab · outbound
This paper cites and Braun, H.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Braun, H
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.306454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation eea572f5-4d30-4393-b5c5-794629c95ca1 · outbound
This paper cites A stochastic gradient method with an exponential convergence \_rate for finite training sets.
MARS: Unleashing the Power of Variance Reduction for Training Large Models A stochastic gradient method with an exponential convergence \_rate for finite training sets
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.950051Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7709864d-3762-4722-a6f1-245e4196a9ce · outbound
This paper cites L., Bhagavatula, C., and Choi, Y.
MARS: Unleashing the Power of Variance Reduction for Training Large Models L., Bhagavatula, C., and Choi, Y
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.262329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ed306893-a7be-47ee-b441-72d8d69ab381 · outbound
This paper cites English Conversational Telephone Speech Recognition by Humans and Machines.
MARS: Unleashing the Power of Variance Reduction for Training Large Models English Conversational Telephone Speech Recognition by Humans and Machines
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T19:43:02.356237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b4caae8a-a9b1-4331-a710-0b162f46e2bc · outbound
This paper cites and Smola, A.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Smola, A
Reference 71
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.966520Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85de6cc9-7d30-49f6-baaa-a23a13a6ab46 · outbound
This paper cites Iterative berechnung der reziproken matrix.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Iterative berechnung der reziproken matrix
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.209999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8bbaeb57-1417-41df-9d90-d1be52929e5c · outbound
This paper cites and Zhang, T.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Zhang, T
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.173456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c262343c-682c-4473-8583-183ee134a835 · outbound
This paper cites and Stern, M.
MARS: Unleashing the Power of Variance Reduction for Training Large Models and Stern, M
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.983245Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 840b886c-4ca4-4976-9d25-026b41b288c8 · outbound
This paper cites A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale.
MARS: Unleashing the Power of Variance Reduction for Training Large Models A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.988790Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d974669-e5d9-4404-b931-c8068638f8c8 · outbound
This paper cites Dropout: a simple way to prevent neural networks from overfitting.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Dropout: a simple way to prevent neural networks from overfitting
Reference 76
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:01.993564Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfa80dc0-fdc0-4942-8de4-99571633fcc4 · outbound
This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.107413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0024312a-7550-4bd0-ada2-61fad6a32166 · outbound
This paper cites Attention is all you need.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Attention is all you need
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.005034Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a752e3d-2adc-4526-a48a-4cfed46f2a60 · outbound
This paper cites SOAP: Improving and Stabilizing Shampoo using Adam.
MARS: Unleashing the Power of Variance Reduction for Training Large Models SOAP: Improving and Stabilizing Shampoo using Adam
Reference 79
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.010064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 012af07a-9e4d-4b3d-836a-77b90ec463da · outbound
This paper cites Spiderboost and momentum: Faster variance reduction algorithms.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Spiderboost and momentum: Faster variance reduction algorithms
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.075581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b3dc8bbf-c6f3-4315-8dad-6e039b3dea6a · outbound
This paper cites Adagrad stepsizes: Sharp convergence over nonconvex landscapes.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Adagrad stepsizes: Sharp convergence over nonconvex landscapes
Reference 81
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.023654Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be36d960-c952-4b93-986c-5cdb9aa09499 · outbound
This paper cites Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models
Reference 82
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.032612Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf3a7227-59be-4ac9-a24e-864cf24dc490 · outbound
This paper cites Quick and (not so) dirty: Unsupervised selection of justification sentences for multi-hop question answering.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Quick and (not so) dirty: Unsupervised selection of justification sentences for multi-hop question answering
Reference 83
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.039030Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08c21778-335f-47a5-9962-26cecaa098da · outbound
This paper cites A Coefficient Makes SVRG Effective.
MARS: Unleashing the Power of Variance Reduction for Training Large Models A Coefficient Makes SVRG Effective
Reference 84
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T19:43:02.285588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a3c723bf-65ea-48d1-8cee-0424688e03d4 · outbound
This paper cites Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.050371Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d6dde87-279b-4b4a-b5b7-79bfaf0dd268 · outbound
This paper cites ADADELTA: An Adaptive Learning Rate Method.
MARS: Unleashing the Power of Variance Reduction for Training Large Models ADADELTA: An Adaptive Learning Rate Method
Reference 86
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.057300Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11b4d049-9e91-4b71-bc3f-4cd504d7f7fc · outbound
This paper cites Hellaswag: Can a machine really finish your sentence? In Korhonen, A., Traum, D.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Hellaswag: Can a machine really finish your sentence? In Korhonen, A., Traum, D
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.063883Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2fd7335a-5e17-4e65-8923-dc475745f55a · outbound
This paper cites OPT: Open Pre-trained Transformer Language Models.
MARS: Unleashing the Power of Variance Reduction for Training Large Models OPT: Open Pre-trained Transformer Language Models
Reference 88
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.069466Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 107f6011-4904-4dc7-b21d-236b708babfd · outbound
This paper cites Adam can converge without any modification on update rules.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Adam can converge without any modification on update rules
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:03.004859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c62ad04f-5834-4868-a3e1-459c08c6a12a · outbound
This paper cites Deconstructing What Makes a Good Optimizer for Language Models.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Deconstructing What Makes a Good Optimizer for Language Models
Reference 90
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.080704Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e1daf55-ba58-4897-a71d-419a408daae3 · outbound
This paper cites Stochastic nested variance reduction for nonconvex optimization.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Stochastic nested variance reduction for nonconvex optimization
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:02.984590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 54a450a1-df44-4fc5-b952-58b06cdd5cb6 · outbound
This paper cites Towards understanding convergence and generalization of adamw.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Towards understanding convergence and generalization of adamw
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T19:43:02.960616Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5465fbce-eb1c-4d54-a499-e15f24becaf1 · outbound
This paper cites C., Dvornek, N., Papademetris, X., and Duncan, J.
MARS: Unleashing the Power of Variance Reduction for Training Large Models C., Dvornek, N., Papademetris, X., and Duncan, J
Reference 93
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.099002Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad9cbca0-2e2f-4589-ae04-316cdc5154a9 · outbound
This paper cites @esa (Ref.
MARS: Unleashing the Power of Variance Reduction for Training Large Models @esa (Ref
Reference 94
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.104287Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 209059a6-ecd1-410f-926f-bbbd5bc28d06 · outbound
This paper cites an unresolved cited work.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 95
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:43:02.110478Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bafc3be5-4fca-4f01-9a61-c7cbb5b25d94 · outbound
This paper cites an unresolved cited work.
MARS: Unleashing the Power of Variance Reduction for Training Large Models Unresolved cited work
Reference 96
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 82111d87-d0d8-4a25-a1e8-c4c8250ce563 · inbound
Physics of Skill Learning cites this paper.
Physics of Skill Learning MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5c46eee-9c29-44dd-865b-18abd9153a49 · inbound
Training Deep Learning Models with Norm-Constrained LMOs cites this paper.
Training Deep Learning Models with Norm-Constrained LMOs MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 218
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T21:22:37.163437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 073a7acf-1bb7-471c-953e-9a2431184c4a · inbound
Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model cites this paper.
Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 42
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2b38aa2-0c1f-4d77-81d3-f81653fca415 · inbound
Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise cites this paper.
Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 93
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a11c4c39-2f8d-4a92-a779-1caf95de60bb · inbound
DNT: a Deeply Normalized Transformer that can be trained by Momentum SGD cites this paper.
DNT: a Deeply Normalized Transformer that can be trained by Momentum SGD MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 2017
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96305ccd-7657-4bf1-b514-55b65d9d72a7 · inbound
On the Convergence of Muon and Beyond cites this paper.
On the Convergence of Muon and Beyond MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 52
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T15:56:34.038686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 837ee271-41b3-4b54-aa56-2611a1f51e82 · inbound
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization cites this paper.
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T09:46:12.512191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9d406b4a-3007-4676-bb2c-6be5b7626b11 · inbound
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum cites this paper.
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T08:17:36.532381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a767a7f1-1c97-42be-904b-2253a4f48c5c · inbound
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction cites this paper.
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T06:55:26.336736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8f4fb87c-5a45-480f-a4f1-3e547fba4e02 · inbound
CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization cites this paper.
CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T11:55:20.585950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 020babd5-7115-47a9-b824-8019276a58f3 · inbound
Personalized Federated Learning for Gradient Alignment cites this paper.
Personalized Federated Learning for Gradient Alignment MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:50:26.268022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 538e25c1-2441-4600-8506-41ad000ad01b · inbound
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less cites this paper.
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:26:08.551157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1c50ea8b-05e4-4edd-a635-6d520343531d · inbound
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers cites this paper.
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 165
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:38:11.033711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e4e9ed29-c096-40da-a023-b20670e9989e · inbound
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers cites this paper.
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 168
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T18:45:00.292382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8c1a22ae-c71f-4321-af06-e8f23ac2b9cf · inbound
Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics cites this paper.
Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 158
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T08:11:17.303623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b4b54b21-6276-4641-8006-bcd108a796c8 · inbound
MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization cites this paper.
MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T20:18:56.220618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 72e002c3-65e5-4a39-875c-59bb0765fa72 · inbound
One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining cites this paper.
One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:44:18.839572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 92f28a8e-159e-4574-89fd-27f8a1ea7be4 · inbound
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers cites this paper.
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 128
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T22:10:49.683444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd0f53ac-6b1c-47ec-96bf-825f239333de · inbound
Muse: Representation Geometry of Muon Beyond Normalized Momentum cites this paper.
Muse: Representation Geometry of Muon Beyond Normalized Momentum MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T01:58:58.664681Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f32997d-8304-4d9a-9fa0-e4c2397f32b3 · inbound
OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining cites this paper.
OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining MARS: Unleashing the Power of Variance Reduction for Training Large Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:30:38.237293Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.