Paper Citation Record · LEDGER

Best Policy Learning from Trajectory Preference Feedback

As of 12 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2501.18873.

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

pith.paper-citation-record.v1
2501.18873 v4

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

12 of 12 outbound references displayed

verified exact2
verified fuzzy8
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c957e81-5093-48bc-8e18-58bd8cce6e4b · outbound

This paper cites URLhttp://www.jstor.org/ stable/2334029.

Best Policy Learning from Trajectory Preference Feedback URLhttp://www.jstor.org/ stable/2334029

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:57:33.842172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9716bf0d-1090-4ba4-bbcc-d596d3baf637 · outbound

This paper cites Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale.

Best Policy Learning from Trajectory Preference Feedback Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:57:33.833975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3b7bc4cc-0b50-4c67-af02-fbefef720f53 · outbound

This paper cites Introduction to the non-asymptotic analysis of random matrices.

Best Policy Learning from Trajectory Preference Feedback Introduction to the non-asymptotic analysis of random matrices

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T04:57:33.791191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d612f9c0-d448-4efa-8b82-0411b37fac82 · outbound

This paper cites Yes, please see Section 2.

Best Policy Learning from Trajectory Preference Feedback Yes, please see Section 2

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.012250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c05d7186-4773-4fd8-bf18-3412c3960122 · outbound

This paper cites Yes, please see Sections 3 and 4, and Appendix A.

Best Policy Learning from Trajectory Preference Feedback Yes, please see Sections 3 and 4, and Appendix A

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.003093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef10d24e-7e89-475a-8eff-424e9868ba93 · outbound

This paper cites Yes, code will be released later.

Best Policy Learning from Trajectory Preference Feedback Yes, code will be released later

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.009197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 08aae76b-2c3a-420e-96e3-23f5f470d362 · outbound

This paper cites Yes, please see Appendix A.

Best Policy Learning from Trajectory Preference Feedback Yes, please see Appendix A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.025666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f141bf8e-9d16-4764-82c8-be0a6d35d179 · outbound

This paper cites xp1´xq.fis a concave function. We have for anyiP t0,1u, Prpπpiq k ‰π ‹q “E.

Best Policy Learning from Trajectory Preference Feedback xp1´xq.fis a concave function. We have for anyiP t0,1u, Prpπpiq k ‰π ‹q “E

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.015513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 22f8b4bc-eb67-4c6b-97ec-17f90f2be540 · outbound

This paper cites Notice that the eachchpsq is the difference of two binomial random variablesb1 „BinpN, 1 ´γ β,λ,N q andb 2 „BinpN, γ β,λ,N q.

Best Policy Learning from Trajectory Preference Feedback Notice that the eachchpsq is the difference of two binomial random variablesb1 „BinpN, 1 ´γ β,λ,N q andb 2 „BinpN, γ β,λ,N q

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.018840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d16f3d24-4952-4cfc-a931-f76e293903b1 · outbound

This paper cites argmax θ,ϑ,η Prpθ, ϑ, η|D kq.

Best Policy Learning from Trajectory Preference Feedback argmax θ,ϑ,η Prpθ, ϑ, η|D kq

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.006114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b140fbc3-57ef-40ee-8683-848dd13eb80e · outbound

This paper cites Similar idea has been proposed to estimate the expertise level in imitation learning Beliaev et al.

Best Policy Learning from Trajectory Preference Feedback Similar idea has been proposed to estimate the expertise level in imitation learning Beliaev et al

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T04:57:34.028926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c844c675-38e8-4251-b014-773d062a375d · outbound

This paper cites 1 diam ` Ft|xt ˘ ďα`Cpd^Tq `2δ T ? dT , whereδ T “max 1ďtďT diam ` Ft|x1:t ˘ andd“dim E pF, αq. Lemma B.8.If pβt ě0|tPNq is a nondecreasing sequence andFt :“.

Best Policy Learning from Trajectory Preference Feedback 1 diam ` Ft|xt ˘ ďα`Cpd^Tq `2δ T ? dT , whereδ T “max 1ďtďT diam ` Ft|x1:t ˘ andd“dim E pF, αq. Lemma B.8.If pβt ě0|tPNq is a nondecreasing sequence andFt :“

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-05-23T04:57:34.022420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.