Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism | Paper Citation Record

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unresolved cited work

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This paper cites Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes

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This paper cites Qwen Technical Report.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Qwen Technical Report

Reference 3

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This paper cites Coupling adaptive batch sizes with learning rates.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Coupling adaptive batch sizes with learning rates

Reference 4

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This paper cites Stable LM 2 1.6B Technical Report.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Stable LM 2 1.6B Technical Report

Reference 5

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This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 6

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unresolved cited work

Reference 7

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This paper cites Adaptive sampling strategies for stochastic optimization.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Adaptive sampling strategies for stochastic optimization

Reference 8

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This paper cites Curtis, and Jorge Nocedal.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Curtis, and Jorge Nocedal

Reference 9

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This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism JAX: composable transformations of Python+NumPy programs, 2018

Reference 10

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unresolved cited work

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This paper cites Byrd, Gillian M.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Byrd, Gillian M

Reference 12

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This paper cites Big Batch SGD: Automated Inference using Adaptive Batch Sizes.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Big Batch SGD: Automated Inference using Adaptive Batch Sizes

Reference 13

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This paper cites Automated inference with adaptive batches.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Automated inference with adaptive batches

Reference 14

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This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 15

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This paper cites DeepSeek-V3 Technical Report.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism DeepSeek-V3 Technical Report

Reference 16

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This paper cites AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks

Reference 17

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism An image is worth 16x16 words: Transformers for image recognition at scale

Reference 18

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This paper cites Susskind, and Armand Joulin.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Susskind, and Armand Joulin

Reference 19

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This paper cites PyTorch Lightning, 2019.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism PyTorch Lightning, 2019

Reference 20

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This paper cites Friedlander and Mark Schmidt.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Friedlander and Mark Schmidt

Reference 21

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This paper cites Compiling machine learning programs via high-level tracing.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Compiling machine learning programs via high-level tracing

Reference 22

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This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Gemma 2: Improving Open Language Models at a Practical Size

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This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Gemma: Open Models Based on Gemini Research and Technology

Reference 24

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This paper cites OpenLLaMA: An open reproduction of LLaMA, May 2023.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism OpenLLaMA: An open reproduction of LLaMA, May 2023

Reference 25

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This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

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This paper cites OLMo: Accelerating the Science of Language Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism OLMo: Accelerating the Science of Language Models

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This paper cites Scaling laws for single-agent reinforcement learning.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Scaling laws for single-agent reinforcement learning

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This paper cites Train longer, generalize better: closing the generalization gap in large batch training of neural networks.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Train longer, generalize better: closing the generalization gap in large batch training of neural networks

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Mistral 7B

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This paper cites GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length

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This paper cites AdaScale SGD: A user-friendly algorithm for distributed training.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism AdaScale SGD: A user-friendly algorithm for distributed training

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This paper cites Scaling Laws for Neural Language Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Scaling Laws for Neural Language Models

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This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism On large-batch training for deep learning: Generalization gap and sharp minima

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This paper cites Better theory for SGD in the nonconvex world.Transactions on Machine Learning Research, 2023.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Better theory for SGD in the nonconvex world.Transactions on Machine Learning Research, 2023

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This paper cites Kingma and Jimmy Lei Ba.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Kingma and Jimmy Lei Ba

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This paper cites Reducing activation recomputation in large transformer models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Reducing activation recomputation in large transformer models

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unresolved cited work

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This paper cites Noise is not the main factor behind the gap between SGD and Adam on transformers, but sign descent might be.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Noise is not the main factor behind the gap between SGD and Adam on transformers, but sign descent might be

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This paper cites Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

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This paper cites Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods

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This paper cites AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods

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This paper cites Orr, and Klaus Robert Müller.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Orr, and Klaus Robert Müller

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This paper cites GShard: Scaling giant models with conditional computation and automatic sharding.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism GShard: Scaling giant models with conditional computation and automatic sharding

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This paper cites The stability-efficiency dilemma: Investigating sequence length warmup for training GPT models.Advances in Neural Information Processing Systems (NeurIPS), 2022.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism The stability-efficiency dilemma: Investigating sequence length warmup for training GPT models.Advances in Neural Information Processing Systems (NeurIPS), 2022

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This paper cites PyTorch distributed: Experiences on accelerating data parallel training.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism PyTorch distributed: Experiences on accelerating data parallel training

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This paper cites TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training

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This paper cites LitGPT, 2023.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism LitGPT, 2023

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Observation 9a41a052-d67d-4158-85f3-5931beda7774 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism The Llama 3 Herd of Models

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Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Decoupled weight decay regularization

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Observation d34ffa08-e6a3-40e1-b3e0-5cd1ba38d35a · outbound

This paper cites An Empirical Model of Large-Batch Training.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism An Empirical Model of Large-Batch Training

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This paper cites Efficient large-scale language model training on GPU clusters using Megatron-LM.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Efficient large-scale language model training on GPU clusters using Megatron-LM

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This paper cites AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size

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Observation de7bce79-ad55-4a69-8474-051b04a9546c · outbound

This paper cites Toward Understanding Why Adam Converges Faster Than SGD for Transformers.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Toward Understanding Why Adam Converges Faster Than SGD for Transformers

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This paper cites Nemotron-4 15B Technical Report.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Nemotron-4 15B Technical Report

Reference 56

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Observation 8a8dd1b2-4148-4ef9-b20f-d797a19836dc · outbound

This paper cites PyTorch: An imperative style, high-performance deep learning library.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism PyTorch: An imperative style, high-performance deep learning library

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.479112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4137c201-4efe-4573-8233-8ca7b0e2ad11 · outbound

This paper cites Large scale language modeling: Converging on 40GB of text in four hours.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Large scale language modeling: Converging on 40GB of text in four hours

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.441402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bf88acb2-dc51-432b-a251-2a59cc317473 · outbound

This paper cites SimiGrad: Fine-grained adaptive batching for large scale training using gradient similarity measurement.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism SimiGrad: Fine-grained adaptive batching for large scale training using gradient similarity measurement

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.356127Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9dc0ab40-ecdb-4c15-adbb-fb68b7f9e0b9 · outbound

This paper cites an unresolved cited work.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unresolved cited work

Reference 60

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Observation 6f68f0b6-4b00-4022-8e2e-3133f6e05054 · outbound

This paper cites ZeRO: Memory optimizations toward training trillion parameter models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism ZeRO: Memory optimizations toward training trillion parameter models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.334289Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 435361fa-002d-4e5c-8082-e025b9ff84c9 · outbound

This paper cites DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.275184Z

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Observation 413252af-5483-45ae-801c-a90863ecd40b · outbound

This paper cites ZeRO-Offload: Democratizing billion-scale model training.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism ZeRO-Offload: Democratizing billion-scale model training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.232150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 284f4916-6eaa-4116-a4f4-5957e00e21ae · outbound

This paper cites On the different regimes of stochastic gradient descent.Proceedings of the National Academy of Sciences, 121(9):e2316301121, 2024.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism On the different regimes of stochastic gradient descent.Proceedings of the National Academy of Sciences, 121(9):e2316301121, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.219609Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b5b9e4a3-0569-436b-837d-6e4b093c86d9 · outbound

This paper cites Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, and George E

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.125126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c58e921-f588-416c-9b0f-8721c32559fe · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.459144Z

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Observation 63a15759-513c-44eb-a4a2-e140610e06c2 · outbound

This paper cites Smith and Quoc V.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Smith and Quoc V

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:30.031318Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 234a99ec-997c-4c5c-81e8-00d68670c9f9 · outbound

This paper cites Smith, Pieter-Jan Kindermans, and Quoc V.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Smith, Pieter-Jan Kindermans, and Quoc V

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.994206Z

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Observation 366ce334-1037-486c-a24f-3c327afabc8c · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.522233Z

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Observation 6140cea0-75ee-42cf-ad62-feac0f1759e2 · outbound

This paper cites Unraveling the Mystery of Scaling Laws: Part I.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Unraveling the Mystery of Scaling Laws: Part I

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.527070Z

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Observation 40cd2d9e-5b48-473f-8c59-f296d3c16d4b · outbound

This paper cites 2 OLMo 2 Furious.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism 2 OLMo 2 Furious

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.532201Z

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Observation 3a15bb9d-3ca6-4f94-9491-526cf29f2130 · outbound

This paper cites Introducing DBRX: A new state-of-the-art open LLM.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Introducing DBRX: A new state-of-the-art open LLM

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.973315Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 68c1c185-1b47-43b3-a13b-42f30562146f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.626060Z

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Observation ff6ea462-df90-4351-9f61-f16f054ce4e9 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.839689Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 38068dd4-107f-4687-83b4-8cf6ff6ba778 · outbound

This paper cites Meta Lingua: A minimal PyTorch LLM training library, 2024.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Meta Lingua: A minimal PyTorch LLM training library, 2024

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.827044Z

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Observation 1f15343f-3aa1-4764-a255-45564d336b2c · outbound

This paper cites Closing the gap between the upper bound and lower bound of Adam’s iteration complexity.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Closing the gap between the upper bound and lower bound of Adam’s iteration complexity

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.812872Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86ddbe66-ccb9-4fec-8b2f-07de7a059f34 · outbound

This paper cites MicroLlama-300M.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism MicroLlama-300M

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.799649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ed4e73fc-e733-4218-9e62-787e408a693e · outbound

This paper cites Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.663870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b954a9c7-ac0e-4775-8057-031783abea46 · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Baichuan 2: Open Large-scale Language Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.684510Z

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Observation 52d360f0-e63b-4c57-abf8-3f126710905b · outbound

This paper cites Qwen2 Technical Report.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Qwen2 Technical Report

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.733863Z

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Observation 6cdb2a43-e351-4882-944a-b155be40774b · outbound

This paper cites Large batch optimization for deep learning: Training BERT in 76 minutes.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Large batch optimization for deep learning: Training BERT in 76 minutes

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.620830Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 041b3c68-5256-4840-819c-0e3d2633bbbc · outbound

This paper cites Susskind.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Susskind

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.606769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aca9d4ba-94cd-475e-9e9b-83fb0df939aa · outbound

This paper cites How does critical batch size scale in pre-training? InInternational Conference on Learning Representations (ICLR), 2025.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism How does critical batch size scale in pre-training? InInternational Conference on Learning Representations (ICLR), 2025

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.593274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c1b70b2a-0fa1-4414-9fd0-5e12aa5c7f3a · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism TinyLlama: An Open-Source Small Language Model

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.819083Z

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Observation 8a79759a-0bd9-41a0-986e-8732853ebe77 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism OPT: Open Pre-trained Transformer Language Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.823254Z

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Observation 2a1a938c-4694-44ef-8f10-c7c54ec86094 · outbound

This paper cites Why Transformers Need Adam: A Hessian Perspective.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Why Transformers Need Adam: A Hessian Perspective

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.827620Z

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Observation 26383e5e-bdd6-4643-b43f-11e84065244a · outbound

This paper cites Picotron: Distributed training framework for education and research experimentation, 2025.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Picotron: Distributed training framework for education and research experimentation, 2025

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.500772Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3103d643-bf42-404a-9da8-2952792fec22 · outbound

This paper cites first-order term.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism first-order term

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:08:29.489027Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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