REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks | Paper Citation Record

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Unresolved cited work

Reference 1

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This paper cites Pricing for gpu instances.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Pricing for gpu instances

Reference 2

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This paper cites BOINC: A Platform for Volunteer Computing.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks BOINC: A Platform for Volunteer Computing

Reference 3

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This paper cites Anderson, Jeff Cobb, Eric Korpela, Matt Lebofsky, and Dan Werthimer.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Anderson, Jeff Cobb, Eric Korpela, Matt Lebofsky, and Dan Werthimer

Reference 4

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This paper cites PaLM 2 Technical Report.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks PaLM 2 Technical Report

Reference 5

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This paper cites Distributed Deep Learning Using Volunteer Computing-Like Paradigm.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Distributed Deep Learning Using Volunteer Computing-Like Paradigm

Reference 6

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This paper cites How much VRAM do you need for Blender ?, October 2023.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks How much VRAM do you need for Blender ?, October 2023

Reference 7

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This paper cites The untapped potential of idle gpus.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks The untapped potential of idle gpus

Reference 8

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This paper cites slurm on kubernetes.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks slurm on kubernetes

Reference 9

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This paper cites Language Models are Few-Shot Learners.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Language Models are Few-Shot Learners

Reference 10

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This paper cites Gandiva fair : A fair GPU cluster scheduler for deep learning workloads.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Gandiva fair : A fair GPU cluster scheduler for deep learning workloads

Reference 11

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Observation c07fdd71-465f-4cbe-a5cc-c09a9424aab1 · outbound

This paper cites Elastic deep learning in multi-tenant GPU clusters.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Elastic deep learning in multi-tenant GPU clusters

Reference 12

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Observation 8b0bca7a-bad6-498e-9bff-9733d71e7540 · outbound

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Unresolved cited work

Reference 13

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Observation 9313cd6b-4c76-4af1-a9d5-cd3a846ca80e · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks PaLM: Scaling Language Modeling with Pathways

Reference 14

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This paper cites LithOS: An Operating System for Efficient Machine Learning on GPUs.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks LithOS: An Operating System for Efficient Machine Learning on GPUs

Reference 15

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This paper cites The promise of analog deep learning: Recent advances, challenges and opportunities, 2024.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks The promise of analog deep learning: Recent advances, challenges and opportunities, 2024

Reference 16

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This paper cites QLoRA : Efficient finetuning of quantized LLMs , 2023.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks QLoRA : Efficient finetuning of quantized LLMs , 2023

Reference 17

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Observation 5eccdbb8-93c1-4a86-abc8-8b0125ac002d · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding, 2018.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks BERT : Pre-training of deep bidirectional transformers for language understanding, 2018

Reference 18

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This paper cites Cross-timeslot optimization for distributed gpu inference using reinforcement learning.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Cross-timeslot optimization for distributed gpu inference using reinforcement learning

Reference 19

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This paper cites Measuring GPU utilization one level deeper.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Measuring GPU utilization one level deeper

Reference 20

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This paper cites Anderson.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Anderson

Reference 21

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This paper cites Garcia, Rafael Mayo, Enrique S.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Garcia, Rafael Mayo, Enrique S

Reference 22

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This paper cites The Llama 3 Herd of Models.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks The Llama 3 Herd of Models

Reference 23

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Observation eae6241b-d18a-4860-a34b-2a382e4a5ce4 · outbound

This paper cites Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

Reference 24

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This paper cites Henderson, Mathieu Lacage, George F.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Henderson, Mathieu Lacage, George F

Reference 25

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This paper cites Ark: Gpu-driven execution for distributed deep learning.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Ark: Gpu-driven execution for distributed deep learning

Reference 26

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This paper cites A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study

Reference 27

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This paper cites Analysis of large-scale multi-tenant gpu clusters for dnn training workloads.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Analysis of large-scale multi-tenant gpu clusters for dnn training workloads

Reference 28

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks A genetic algorithm-based scheduling method for optimizing GPU utilization in multi-tenant cloud environments

Reference 29

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Unresolved cited work

Reference 30

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Mininet: An instant virtual network on your laptop (or other pc)

Reference 31

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Volunteer computing on mobile devices: State of the art and future research directions

Reference 32

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling

Reference 33

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This paper cites Astraea: A Fair Deep Learning Scheduler for Multi-Tenant GPU Clusters.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Astraea: A Fair Deep Learning Scheduler for Multi-Tenant GPU Clusters

Reference 34

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This paper cites Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

Reference 35

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Resource Scheduling in Edge Computing: A Survey

Reference 36

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing

Reference 37

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This paper cites Themis: Fair and efficient GPU cluster scheduling for ML training.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Themis: Fair and efficient GPU cluster scheduling for ML training

Reference 38

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Fast and Fair Training for Deep Learning in Heterogeneous GPU Clusters

Reference 39

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This paper cites Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Reference 40

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REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks GPT-4 Technical Report

Reference 41

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This paper cites Efficient flow scheduling in distributed deep learning training with echelon formation.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Efficient flow scheduling in distributed deep learning training with echelon formation

Reference 42

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This paper cites Robust speech recognition via large-scale weak supervision, 2023.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Robust speech recognition via large-scale weak supervision, 2023

Reference 43

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Observation 9d13ee33-a1a8-4c4c-b499-5e4e5496a5d1 · outbound

This paper cites CASSINI : Network-aware job scheduling for ML training.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks CASSINI : Network-aware job scheduling for ML training

Reference 44

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Observation 3624f68f-9bf4-49c7-aeba-0f07101be77f · outbound

This paper cites High-resolution image synthesis with latent diffusion models, 2022.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks High-resolution image synthesis with latent diffusion models, 2022

Reference 45

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This paper cites Towards topology aware pre-emptive job scheduling with deep reinforcement learning.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Towards topology aware pre-emptive job scheduling with deep reinforcement learning

Reference 46

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This paper cites DistilBERT , a distilled version of BERT : smaller, faster, cheaper and lighter, 2019.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks DistilBERT , a distilled version of BERT : smaller, faster, cheaper and lighter, 2019

Reference 47

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This paper cites Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices

Reference 48

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This paper cites SDXL 1.0: A new era for generative ai, 2023.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks SDXL 1.0: A new era for generative ai, 2023

Reference 49

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This paper cites Orion: Interference-aware, fine-grained GPU sharing for ML applications.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Orion: Interference-aware, fine-grained GPU sharing for ML applications

Reference 50

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This paper cites Hadar: Heterogeneity-Aware Optimization-Based misc Scheduling for Deep Learning Cluster.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Hadar: Heterogeneity-Aware Optimization-Based misc Scheduling for Deep Learning Cluster

Reference 51

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This paper cites Saladcloud: Rent and share gpus.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Saladcloud: Rent and share gpus

Reference 52

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Observation 5a0e0f2f-f7d2-4701-897f-1d0341bc002d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks LLaMA: Open and Efficient Foundation Language Models

Reference 53

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Observation 9adf2368-9199-4842-9727-138482a9a2d2 · outbound

This paper cites GPU marketplace offerings data, 2025.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks GPU marketplace offerings data, 2025

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.660653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de4ca2f3-aa20-4d6a-aef4-a0b2ff0e83ca · outbound

This paper cites Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:19:19.322507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47ff1c93-35bf-4ff4-a004-2a84fed0276a · outbound

This paper cites Taming GPU fragmentation in large-scale ML clusters with fragmentation gradient descent.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Taming GPU fragmentation in large-scale ML clusters with fragmentation gradient descent

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.649089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d188f373-1666-40a5-a93f-84aa79849b49 · outbound

This paper cites Yan and et al.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Yan and et al

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.638255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e35e8091-7c8c-418b-bcf3-0cd1c19fa928 · outbound

This paper cites GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.626954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 10178f3a-6d29-45a9-93af-08fed10fff13 · outbound

This paper cites Salus: Fine-grained GPU sharing primitives for deep learning applications.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Salus: Fine-grained GPU sharing primitives for deep learning applications

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.615242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 046e46c2-bbd4-4243-89a8-9484a297f3ba · outbound

This paper cites TAG : An automatic framework for topology-aware and heterogeneity-aware distributed DNN training.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks TAG : An automatic framework for topology-aware and heterogeneity-aware distributed DNN training

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:19:19.603228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2bb8d257-8f46-4579-abe1-33f20f8ee644 · outbound

This paper cites Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T17:19:19.272330Z

Source-reported events for the cited work

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