Paper Citation Record · LEDGER

A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2504.12328.

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

pith.paper-citation-record.v1
2504.12328 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T05:56:17.734874Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T05:46:41.076602Z

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 4f5f303e-8ec3-4e6a-8cec-2d4640f9a9f7 · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:48.183065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 91b27b97-6836-4701-b574-f0eba351c387 · inbound

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation cites this paper.

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 125

Resolution: unresolved; no resolver link, observed 2026-08-03T03:04:45.216822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f226c9-a0cd-4f28-9a2f-94e4b5493b28 · inbound

Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization cites this paper.

Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T05:56:17.734874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b80ce6e4-1d60-4892-af25-9ed48cca04e3 · inbound

StoryAlign: Evaluating and Training Reward Models for Story Generation cites this paper.

StoryAlign: Evaluating and Training Reward Models for Story Generation A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:31:07.645525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 35a22826-9bed-4c54-ad0b-4b26450c99e0 · inbound

DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification cites this paper.

DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:24.230952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39a11b7d-857e-4c3a-8c55-237c94b4d1fd · inbound

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation cites this paper.

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.720310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f89c9da0-0fbe-4a96-a8a7-c30ff9d84c95 · inbound

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation cites this paper.

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:59:48.637981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 900a3756-db76-42c6-9c21-a578f0cea061 · inbound

Scalable Token-Level Hallucination Detection in Large Language Models cites this paper.

Scalable Token-Level Hallucination Detection in Large Language Models A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:52:22.393010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 61303caa-455b-4c9e-92bd-11e43e669e5c · inbound

SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice cites this paper.

SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:46:41.078116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0b1b94c1-dcdb-4cd0-8d61-8945a6a2d2d5 · inbound

Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Stateful Personal Agents cites this paper.

Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Stateful Personal Agents A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T11:04:44.592375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.