Paper Citation Record · LEDGER

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

As of 11 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2501.12486.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.12486 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:17:00.114314Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:44:48.441119Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T20:44:48.658319Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation ce9ef419-260e-4283-aa1b-0a5107b71a07 · outbound

This paper cites Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers

Reference 1

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This paper cites Scaling to very very large corpora for natural language disambiguation.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Scaling to very very large corpora for natural language disambiguation

Reference 2

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This paper cites Data scaling laws in NMT : The effect of noise and architecture.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Data scaling laws in NMT : The effect of noise and architecture

Reference 3

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This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 4

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This paper cites Language models are few-shot learners.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Language models are few-shot learners

Reference 5

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This paper cites Maxtext: A framework for training large language models.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Maxtext: A framework for training large language models

Reference 6

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This paper cites Rigging the lottery: Making all tickets winners.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Rigging the lottery: Making all tickets winners

Reference 7

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 8

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This paper cites Roy, and Michael Carbin.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Roy, and Michael Carbin

Reference 9

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This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 10

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This paper cites Scaling laws for sparsely-connected foundation models.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Scaling laws for sparsely-connected foundation models

Reference 11

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This paper cites The State of Sparsity in Deep Neural Networks.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws The State of Sparsity in Deep Neural Networks

Reference 12

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This paper cites Scaling laws for neural machine translation.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Scaling laws for neural machine translation

Reference 13

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws A bit of progress in language modeling

Reference 14

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This paper cites Data and parameter scaling laws for neural machine translation.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Data and parameter scaling laws for neural machine translation

Reference 15

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This paper cites The Llama 3 Herd of Models.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws The Llama 3 Herd of Models

Reference 16

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This paper cites OLM o: Accelerating the science of language models.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws OLM o: Accelerating the science of language models

Reference 17

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Unresolved cited work

Reference 18

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Hassibi, D.G

Reference 19

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This paper cites Channel pruning for accelerating very deep neural networks.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Channel pruning for accelerating very deep neural networks

Reference 20

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Rae, and Laurent Sifre

Reference 21

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Roy, Jonathan Frankle, and Gintare Karolina Dziugaite

Reference 22

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This paper cites Scaling Laws for Neural Language Models.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Scaling Laws for Neural Language Models

Reference 23

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Accurate neural network pruning requires rethinking sparse optimization

Reference 24

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Optimal brain damage

Reference 25

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Liu and Jorge Nocedal

Reference 26

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Step: learning n:m structured sparsity masks from scratch with precondition

Reference 27

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This paper cites Deep double descent: Where bigger models and more data hurt.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Deep double descent: Where bigger models and more data hurt

Reference 28

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This paper cites Efficient Stagewise Pretraining via Progressive Subnetworks.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Efficient Stagewise Pretraining via Progressive Subnetworks

Reference 29

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Larsen, Jonathan Frankle, Surya Ganguli, and Gintare Karolina Dziugaite

Reference 30

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws AC / DC : Alternating compressed/decompressed training of deep neural networks

Reference 31

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Unresolved cited work

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Comparing rewinding and fine-tuning in neural network pruning

Reference 33

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws On the predictability of pruning across scales

Reference 34

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Creating sparse gpt-3 models with iterative pruning

Reference 35

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This paper cites Beyond chinchilla-optimal: Accounting for inference in language model scaling laws.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Beyond chinchilla-optimal: Accounting for inference in language model scaling laws

Reference 36

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

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 37

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws A simple and effective pruning approach for large language models

Reference 38

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Llama 2: Open Foundation and Fine-Tuned Chat Models

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This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 40

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Step: Staged parameter-efficient pre-training for large language models

Reference 41

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Masked structural growth for 2x faster language model pre-training

Reference 42

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This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 43

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws write newline

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws @esa (Ref

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Unresolved cited work

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The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws Unresolved cited work

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Pith citing papers

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QuEST: Stable Training of LLMs with 1-Bit Weights and Activations cites this paper.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

Reference 2025

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