Paper Citation Record · LEDGER

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2502.09003.

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

pith.paper-citation-record.v1
2502.09003 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:03:44.735227Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:42:14.467357Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:06:28.124222Z

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy3
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91196593-cbd3-4fc1-ac6a-1426435b932b · outbound

This paper cites PaLM 2 Technical Report.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models PaLM 2 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.536917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b702a118-15d6-4df0-a959-82e74271d743 · outbound

This paper cites Low-Rank Quantization-Aware Training for LLMs.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Low-Rank Quantization-Aware Training for LLMs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.553156Z

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Observation d8b264b3-16a6-44c3-af21-2e84216fb888 · outbound

This paper cites an unresolved cited work.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:03:45.394408Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 76a4f2b4-3362-49df-a6f7-4b3c9fb5517b · outbound

This paper cites Evaluating Large Language Models Trained on Code.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Evaluating Large Language Models Trained on Code

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.563345Z

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Observation bbeb088b-ee50-40aa-a380-74eb3ef543cb · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.569109Z

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Observation 9ff8c709-d6c4-4f0d-9dd4-1d298af90e89 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Training Verifiers to Solve Math Word Problems

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.573985Z

Source-reported events for the cited work

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Observation 2c7bbd9e-662b-4c71-a055-6c65c17b0897 · outbound

This paper cites BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.578813Z

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Observation 188b7196-5e3c-4c06-a85b-bd370be08691 · outbound

This paper cites The Llama 3 Herd of Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models The Llama 3 Herd of Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.583841Z

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Observation 658cba5f-2a5f-4553-a33f-3deabaab3908 · outbound

This paper cites Extreme Compression of Large Language Models via Additive Quantization.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Extreme Compression of Large Language Models via Additive Quantization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.588832Z

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Observation ffd278fc-b5f0-42b9-8390-767bf3042365 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Measuring Massive Multitask Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.598381Z

Source-reported events for the cited work

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Observation c04afc0c-c6e3-4371-87c7-d4323d6bb726 · outbound

This paper cites OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.613095Z

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Observation 1cb90dd1-e410-45f2-af32-a0ebcc7e9b92 · outbound

This paper cites Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:03:45.089611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f871fe7-72fa-4ae7-9e6a-6590a6868103 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.628672Z

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Observation ad37036c-436e-405f-9b8f-ae63a4561ea9 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.633487Z

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Observation c06bde00-4f54-477a-8dbb-b25bd8c9a328 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.638140Z

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Observation 870ab7bb-5903-483c-811b-e28872c54edd · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models SpinQuant: LLM quantization with learned rotations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.643014Z

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Observation 231e96d9-cbf8-4fab-bb14-2ed9419f4af5 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.658008Z

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Observation 45500c75-d4ab-429d-81d9-7d08780ac842 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.662978Z

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Observation 8cae0720-9231-46ba-be5f-2a00ed610710 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models LaMDA: Language Models for Dialog Applications

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.667576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e187d0bf-8b0a-4ce0-b47c-804f16b4a4d1 · outbound

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

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.672666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b18d4993-1044-44d9-a6ac-83d11e28ebb1 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.677581Z

Source-reported events for the cited work

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Observation b3c12fc4-65e4-4635-ad6f-fdabf39500e9 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.687117Z

Source-reported events for the cited work

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Observation 68b05412-62f4-4bfb-b1b3-8ccfb6bc6ecf · outbound

This paper cites A Survey of Resource-efficient LLM and Multimodal Foundation Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models A Survey of Resource-efficient LLM and Multimodal Foundation Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.691600Z

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Observation 3ae27ece-5255-4b0c-b991-0cb5d2e17b75 · outbound

This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models OneBit: Towards Extremely Low-bit Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.696274Z

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Observation ffbd40e8-6e66-4f06-9f31-efd726e19250 · outbound

This paper cites Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.701302Z

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Observation e57ebf45-10fb-4f0c-bb32-09a3a8ee1493 · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.706092Z

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Observation 6d83edae-8300-418e-804e-c3c73be701fb · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.710819Z

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Observation 1fc42e5e-4a2a-4516-addd-54e2e4bdf05d · outbound

This paper cites Proof of Theorem 4.3 Proof.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Proof of Theorem 4.3 Proof

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:03:45.425934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7ada6363-8aee-4207-b71e-e508b1775fe8 · outbound

This paper cites an unresolved cited work.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:03:45.410080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75963ffa-387f-4a90-8824-3fd9a7c30738 · outbound

This paper cites Benchmark TruthfulQA MMLU-Pro BigBenchHard AGIEval GSM8K Math # shot 6 0 3 0 8 4 Metric Acc (mc1) EM EM Acc EM EM CoT ✓ ✗ ✗ ✗ ✓ ✗ D.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Benchmark TruthfulQA MMLU-Pro BigBenchHard AGIEval GSM8K Math # shot 6 0 3 0 8 4 Metric Acc (mc1) EM EM Acc EM EM CoT ✓ ✗ ✗ ✗ ✓ ✗ D

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:03:45.378919Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e0e5f4a-1980-496a-9823-f47744d304cc · outbound

This paper cites (Left) Relative reduction rates of quantization error, calculated as Error w/o rotation−Error w/ rotation Error w/o rotation × 100%.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models (Left) Relative reduction rates of quantization error, calculated as Error w/o rotation−Error w/ rotation Error w/o rotation × 100%

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:03:45.362651Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05ea39b5-38dd-4da7-85fb-281371ae2a79 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 1976

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.593599Z

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Observation b0880f39-3b93-4bdc-aa5d-90bb0e6fabbc · outbound

This paper cites Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.623807Z

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Observation 4e8b8003-b722-4dab-8f7d-1238ffe1b854 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.608247Z

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Observation 38879018-8b76-4517-8e84-41a142a399dd · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.682214Z

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Observation a1a41cf7-ac66-42ce-acd9-c437eca29d17 · outbound

This paper cites The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.603283Z

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Observation 21a7b5d1-8228-47b8-99f5-ce06768a87b9 · outbound

This paper cites ProxQuant: Quantized Neural Networks via Proximal Operators.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models ProxQuant: Quantized Neural Networks via Proximal Operators

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.548070Z

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Observation dd72ea8d-4238-480b-89eb-25cb911e6a7b · outbound

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

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.648169Z

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Observation 86f3be89-c7cd-4f63-b803-edd8b29b2b35 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.542756Z

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Observation 9062b3a6-1094-40ea-a911-55dc8b9695a3 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.558311Z

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Unavailable: canonical work link unavailable.

Observation 03188f45-46f6-4947-a8fb-a08b0587913d · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.653134Z

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Pith citing papers

Observation af633ce9-fa78-45fe-9e8d-52d6ada65d07 · inbound

Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization cites this paper.

Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:14.467357Z

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Observation 0d447c84-afb6-4f22-b0d9-8eb50b8259cb · inbound

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:06:28.231100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f32c2bc-feee-4828-9510-00a671ebd2bd · inbound

GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries cites this paper.

GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T09:31:59.865124Z

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Unavailable: canonical work link unavailable.