Paper Citation Record · LEDGER

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability

As of 11 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.11432.

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

pith.paper-citation-record.v1
2607.11432 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T05:38:58.035337Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 554e12db-c1dc-4230-9f60-07df4766662e · outbound

This paper cites Concrete Problems in AI Safety.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Concrete Problems in AI Safety

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation f0f231be-1acc-4612-8e83-f3a3a9ba6fef · outbound

This paper cites Active Preference-Based Gaussian Process Regression for Reward Learning.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Active Preference-Based Gaussian Process Regression for Reward Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 4c80310a-3406-43f2-b43a-25b1b79ce8e8 · outbound

This paper cites an unresolved cited work.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 573a5b80-221b-4ab5-b35d-a7b27625e9ea · outbound

This paper cites Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 9f42a0cd-1d04-476e-80ce-b0d5d087b732 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Proximal Policy Optimization Algorithms

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 641d77a2-c8d0-49a0-9ccf-8864d3194904 · outbound

This paper cites Multi-Objective Reinforcement Learning.MORL addresses the challenge of optimizing multiple, often conflicting, criteria to discover a set of Pareto-optimal policies.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Multi-Objective Reinforcement Learning.MORL addresses the challenge of optimizing multiple, often conflicting, criteria to discover a set of Pareto-optimal policies

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 77ecc75e-c5a9-4ce1-92cb-e8c05c63fd82 · outbound

This paper cites The CbRL setting has the same learning goal, i.e., recovering the Pareto frontier.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability The CbRL setting has the same learning goal, i.e., recovering the Pareto frontier

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

Source-reported events for the cited work

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Observation 9038cc68-5e3c-4179-aa88-30ed3e1f79d5 · outbound

This paper cites an unresolved cited work.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation f577a497-ff57-44a1-a624-95479a8935e1 · outbound

This paper cites weight” to refer to the scalarization weight of the multiple objectives rather than the term “preference.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability weight” to refer to the scalarization weight of the multiple objectives rather than the term “preference

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

Source-reported events for the cited work

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Observation 14d1e812-55a5-4c12-979f-51514a55e527 · outbound

This paper cites In recent years, novel rationality models have been proposed to tackle specific limitations of BT.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability In recent years, novel rationality models have been proposed to tackle specific limitations of BT

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation dd800939-999e-49a1-8751-35540713434e · outbound

This paper cites Finally, thegeneral preference optimization (Zhang et al.,.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Finally, thegeneral preference optimization (Zhang et al.,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation d12fb97f-66ef-41bf-b6db-5624aba5c736 · outbound

This paper cites an unresolved cited work.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation c0526321-cdfe-4ba5-bd8a-30c18673c5b2 · outbound

This paper cites threshold of the sensory perception of the judge.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability threshold of the sensory perception of the judge

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 39fd50f1-3fbe-4eb1-9687-85274b0d49ed · outbound

This paper cites The standard approach in the presence of incomparability in PbRL is to discard the query and consider the sample as erroneous (Christiano et al., 2017).

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability The standard approach in the presence of incomparability in PbRL is to discard the query and consider the sample as erroneous (Christiano et al., 2017)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 7746d8fb-8d28-41de-9673-0c30bb03e7a6 · outbound

This paper cites With respect toα, we have: ››∇αhąpτ, τ 1|θq ›› 2 “0, ››∇αhăpτ, τ 1|θq ›› 2 “0,››∇αh—pτ, τ 1|θq ›› 2 “1, ››∇αh∥pτ, τ 1|θq ›› 2 “0.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability With respect toα, we have: ››∇αhąpτ, τ 1|θq ›› 2 “0, ››∇αhăpτ, τ 1|θq ›› 2 “0,››∇αh—pτ, τ 1|θq ›› 2 “1, ››∇αh∥pτ, τ 1|θq ›› 2 “0

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 8db5559e-cbee-458d-9dc1-aff1040a8a0e · outbound

This paper cites an unresolved cited work.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation b6d90144-a987-4d32-b713-d28d1f8cfcb2 · outbound

This paper cites First, we discuss the methodology of our experiments in Appendix D.1, covering the procedures for generating trajectories and comparison labels.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability First, we discuss the methodology of our experiments in Appendix D.1, covering the procedures for generating trajectories and comparison labels

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

Source-reported events for the cited work

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Observation 0f6386e3-b5ae-4c7c-95bb-acde4d51fd6c · outbound

This paper cites MO-Hopper.We use the mo-hopper-2obj-v5 environment from the MO-Gymnasium suite (Fel- ten et al., 2023), the multi-objective extension of Hopper-v5 (Towers et al., 2025).

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability MO-Hopper.We use the mo-hopper-2obj-v5 environment from the MO-Gymnasium suite (Fel- ten et al., 2023), the multi-objective extension of Hopper-v5 (Towers et al., 2025)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation 7c06ebbf-2c68-4d63-9ceb-e050687b2a5d · outbound

This paper cites The Pareto frontier admits a closed-form solution under the LQR formalism, providing an exact reference for policy-level evaluation.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability The Pareto frontier admits a closed-form solution under the LQR formalism, providing an exact reference for policy-level evaluation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Observation dd652548-bd16-4374-b0b2-2d7c0d388737 · outbound

This paper cites One common practical refinement to improve the overall performance is to employ an ensemble of predictors trained from randomized initializations.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability One common practical refinement to improve the overall performance is to employ an ensemble of predictors trained from randomized initializations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 714c95c1-523b-485a-b87e-0a979c81b9e3 · outbound

This paper cites an unresolved cited work.

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T05:38:58.035337Z

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Pith citing papers

No inbound Pith citation observations are available.