Paper Citation Record · LEDGER

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2301.07784.

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

pith.paper-citation-record.v1
2301.07784 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:07:57.471246Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:58:58.451676Z

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 587b2e84-25d8-4787-bf2e-2c391fe2eb36 · inbound

Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation cites this paper.

Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T14:07:57.471246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 972e325b-4dd9-45f5-bbeb-e80c22762383 · inbound

Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning cites this paper.

Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-01T16:35:50.553809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 74dbb2a3-7c81-4757-82ff-0deabc319379 · inbound

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning cites this paper.

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:58.453398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 18214058-f794-4dd8-bb70-c4ab21e01681 · inbound

Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning cites this paper.

Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:05:44.741097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.