Paper Citation Record · LEDGER

Active Preference Optimization for Sample Efficient RLHF

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2402.10500.

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

pith.paper-citation-record.v1
2402.10500 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:33:51.144721Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T11:54:38.395848Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3c1a0ce5-1a1c-4302-ad9c-3deb7fd487ab · inbound

Reinforcement Learning for Reasoning in Large Language Models with One Training Example cites this paper.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Active Preference Optimization for Sample Efficient RLHF

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:51:05.040421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fc84104b-b3ff-4fd6-aee8-135c2a4557f2 · inbound

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain cites this paper.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain Active Preference Optimization for Sample Efficient RLHF

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:33:51.144721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b56a1c6-51ce-444d-85c0-4266939f0d2e · inbound

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits cites this paper.

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits Active Preference Optimization for Sample Efficient RLHF

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:40.442509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5182579f-4527-4fea-820f-fafc04ade777 · inbound

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts cites this paper.

Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts Active Preference Optimization for Sample Efficient RLHF

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T11:33:53.628034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eed8347-9013-4e61-8a8c-85cc4566353c · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Active Preference Optimization for Sample Efficient RLHF

Reference 178

Resolution: unresolved; no resolver link, observed 2026-08-06T14:13:06.584028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 127f23ca-70cb-44e5-bfda-4e82a1ee6d50 · inbound

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing cites this paper.

Beyond Variance: Prompt-Efficient RLVR via Rare-Event Amplification and Bidirectional Pairing Active Preference Optimization for Sample Efficient RLHF

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:27:36.833890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 83ad6cf0-5e9d-4a37-9fac-fa443207e2d3 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Active Preference Optimization for Sample Efficient RLHF

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:37:28.542340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 654d8ec1-75c3-4deb-af72-177b3e5c065e · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Active Preference Optimization for Sample Efficient RLHF

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:10:10.420023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d63cda43-1747-4513-9716-71d4a312e267 · inbound

Reinforcement Learning from Human Feedback: A Statistical Perspective cites this paper.

Reinforcement Learning from Human Feedback: A Statistical Perspective Active Preference Optimization for Sample Efficient RLHF

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:13:13.649261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6b22369-3ace-4ff6-bdc8-ca9414f4c7a2 · inbound

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback cites this paper.

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Active Preference Optimization for Sample Efficient RLHF

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:46:45.870405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 650112f8-d2a3-42a5-aa76-307741b3e8eb · inbound

MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization cites this paper.

MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization Active Preference Optimization for Sample Efficient RLHF

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:31:24.543726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c01721f9-e770-48ed-b2e5-f67d73a59e35 · inbound

Spectral Souping: A Unified Framework for Online Preference Alignment cites this paper.

Spectral Souping: A Unified Framework for Online Preference Alignment Active Preference Optimization for Sample Efficient RLHF

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.957942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91328711-5186-447a-aa88-0448c01b8788 · inbound

Active Learning for Stochastic Contextual Linear Bandits cites this paper.

Active Learning for Stochastic Contextual Linear Bandits Active Preference Optimization for Sample Efficient RLHF

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.397551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.