Paper Citation Record · LEDGER

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

As of 16 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2602.07764.

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

pith.paper-citation-record.v1
2602.07764 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:33:04.223413Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

14 of 14 outbound references displayed

verified exact0
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 367208e2-3346-453d-b789-ce28b1603cea · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.187857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77951548-8b8f-4916-baf2-c4370b614546 · outbound

This paper cites A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.179060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1102797b-f85c-4156-b26c-aaf30457b684 · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.194989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7944cded-417b-4130-93cf-2a99628f429d · outbound

This paper cites 16 Decomposed, Diversity-Driven Policy Optimization Then gradient ascent converges to the set of stationary points ofJ(π).

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization 16 Decomposed, Diversity-Driven Policy Optimization Then gradient ascent converges to the set of stationary points ofJ(π)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.198801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37102d1d-272a-45dd-9e15-0eaa11fbe060 · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.191639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 589e2b91-2307-430d-98ee-d1fce62815c7 · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.202501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 788c2421-dd9f-468f-9697-e0c7dfa12f2e · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.205697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e06cd141-afc1-441e-b972-60b8ec1386f9 · outbound

This paper cites Then lim t→∞ ∥∇J(θ t)∥= 0almost surely.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Then lim t→∞ ∥∇J(θ t)∥= 0almost surely

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.209291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4e88ab7-453b-47b8-94d0-02fa5f27cc80 · outbound

This paper cites This environment showcases D3PO’s ability to reliably improve both reward quality and the structure of Pareto-optimal solutions.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization This environment showcases D3PO’s ability to reliably improve both reward quality and the structure of Pareto-optimal solutions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.213020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ee189ac-1bb1-40cc-9b7f-d738107d7a79 · outbound

This paper cites an unresolved cited work.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.216280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad509f00-5405-418c-bcc5-b4a4c19d15a3 · outbound

This paper cites These results highlight D 3PO’s robustness in high-dimensional, unstable regimes where conventional MORL baselines often struggle.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization These results highlight D 3PO’s robustness in high-dimensional, unstable regimes where conventional MORL baselines often struggle

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.219958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b854ae7-ad8b-4bd3-9997-df649c7a83ab · outbound

This paper cites Importantly, in nearly all such cases, D 3PO still attains better mean performance, but the tests are dominated by large variance, typically from C-MORL.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Importantly, in nearly all such cases, D 3PO still attains better mean performance, but the tests are dominated by large variance, typically from C-MORL

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.223413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d017fcf7-0138-492d-9d84-c2e46d78296a · outbound

This paper cites Pareto Set Learning for Multi-Objective Reinforcement Learning.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization Pareto Set Learning for Multi-Objective Reinforcement Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.174258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d761e5c-c4fe-4972-a8b5-e03497ac9220 · outbound

This paper cites 10 Decomposed, Diversity-Driven Policy Optimization A.

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization 10 Decomposed, Diversity-Driven Policy Optimization A

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:04.183177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.