Paper Citation Record · LEDGER

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.22565.

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

pith.paper-citation-record.v1
2507.22565 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:43:15.053074Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact1
verified fuzzy10
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 985350ad-256c-4aa8-8751-dc548b901d90 · outbound

This paper cites write newline.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:12.570822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8abdf2c0-ff0d-4328-b584-8b16e418028c · outbound

This paper cites Deep learning with differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Deep learning with differential privacy

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:12.612617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2ad7d1f-ad3d-4308-b436-93d57a34eef6 · outbound

This paper cites Differentially private learning with adaptive clipping.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Differentially private learning with adaptive clipping

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:12.726662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7481d484-1591-426e-82c8-afeb2e7c19a9 · outbound

This paper cites Automatic clipping: Differentially private deep learning made easier and stronger.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Automatic clipping: Differentially private deep learning made easier and stronger

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:18.250651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a0b9f5c-1b8c-47be-bf25-ec3c3eab7e36 · outbound

This paper cites Membership inference attacks from first principles.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Membership inference attacks from first principles

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:18.047519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce092f6c-9284-4359-b047-a71a13aa548c · outbound

This paper cites Mistral 7B.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Mistral 7B

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:12.967584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70ff7309-dae0-4229-b72b-eeddfdf19d6a · outbound

This paper cites Multi-step reinforcement learning: A unifying algorithm.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Multi-step reinforcement learning: A unifying algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:17.746341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a06448f1-1099-4dbb-9e67-8fd87682c41c · outbound

This paper cites Gaussian differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Gaussian differential privacy

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:17.488113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e95ed40a-534f-4ff4-b1cf-721211ad5858 · outbound

This paper cites The algorithmic foundations of differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning The algorithmic foundations of differential privacy

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.296935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b31b0c60-b8fd-4864-b3e9-c0e71576566b · outbound

This paper cites Geoclip: Geometry-aware clipping for differentially private sgd.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Geoclip: Geometry-aware clipping for differentially private sgd

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.372434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 821d5bc7-de37-45f2-90a9-d26c1435b8e7 · outbound

This paper cites The Llama 3 Herd of Models.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.448370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 387ec0f5-b0c5-4e01-a474-b56639255daf · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.554740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb5c481-ebe8-4050-9e28-48272d85294a · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Lora: Low-rank adaptation of large language models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.646796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bca0c17-51cb-458a-9840-25a16c71a938 · outbound

This paper cites Large Language Models Can Be Strong Differentially Private Learners.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Large Language Models Can Be Strong Differentially Private Learners

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:13.733634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 010f1573-043d-4ca0-bf7c-7d0bb339cff2 · outbound

This paper cites Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:17.202689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc7500e7-d9b0-457f-bb50-8b587a07398e · outbound

This paper cites Differentially private low-rank adaptation of large language model using federated learning.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Differentially private low-rank adaptation of large language model using federated learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:16.921676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c05df738-3665-4014-8263-831ce653ea69 · outbound

This paper cites R \'e nyi differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning R \'e nyi differential privacy

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:14.074621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165e00b2-3671-43e1-8755-3b688ac4e943 · outbound

This paper cites Scalable Private Learning with PATE.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Scalable Private Learning with PATE

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:14.189203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69586a21-e7e8-4143-8853-2aa87e115a5b · outbound

This paper cites AdaCliP: Adaptive Clipping for Private SGD.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning AdaCliP: Adaptive Clipping for Private SGD

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:14.323680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4455fa88-68da-4aa4-874a-a79564985957 · outbound

This paper cites Efficient Hyperparameter Optimization for Differentially Private Deep Learning.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Efficient Hyperparameter Optimization for Differentially Private Deep Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:43:15.351648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6978fb2f-690e-4daf-a197-fc808abe7abd · outbound

This paper cites Language models are unsupervised multitask learners.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Language models are unsupervised multitask learners

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T11:43:14.600661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaedad78-ee72-428c-b9de-c52db8c6ffd9 · outbound

This paper cites Dc-sgd: Differentially private sgd with dynamic clipping through gradient norm distribution estimation.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Dc-sgd: Differentially private sgd with dynamic clipping through gradient norm distribution estimation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:16.642301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbb0f929-ebb7-45dc-b1cb-379a1eb11940 · outbound

This paper cites Differentially private learning with per-sample adaptive clipping.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Differentially private learning with per-sample adaptive clipping

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:16.374582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 504d02dd-1eea-46d4-9f44-8abe8802c857 · outbound

This paper cites A concurrent federated reinforcement learning for iot resources allocation with local differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning A concurrent federated reinforcement learning for iot resources allocation with local differential privacy

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:16.137920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3aae371a-4be4-4f1a-a4ef-c866171c9f65 · outbound

This paper cites Poission subsampled r \'e nyi differential privacy.

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning Poission subsampled r \'e nyi differential privacy

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:43:15.871149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.