Paper Citation Record · LEDGER

Multi-objective Reinforcement learning from AI Feedback

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.07295.

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

pith.paper-citation-record.v1
2406.07295 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:11:21.962879Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T06:44:00.890441Z

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 e37066ae-2d7d-40b8-8b8e-00bec3edcc6f · inbound

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration cites this paper.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Multi-objective Reinforcement learning from AI Feedback

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.962879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07800d77-66de-4df0-9f86-f2063f4be016 · inbound

Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization cites this paper.

Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization Multi-objective Reinforcement learning from AI Feedback

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:26:04.824950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e39d376c-1f67-42ca-82c6-8f88f92173b4 · inbound

Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR cites this paper.

Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR Multi-objective Reinforcement learning from AI Feedback

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:03:03.436508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fee51cc4-a833-44f0-a300-61d4c998fe69 · inbound

SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front cites this paper.

SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front Multi-objective Reinforcement learning from AI Feedback

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:44:00.892082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 53e6144e-33e8-454b-b059-9793794acb39 · inbound

Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL cites this paper.

Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL Multi-objective Reinforcement learning from AI Feedback

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T10:57:59.414952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.