Paper Citation Record · LEDGER

Learning Guarantee of Reward Modeling Using Deep Neural Networks

As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.06601.

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

pith.paper-citation-record.v1
2505.06601 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:53.918108Z

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 fuzzy5
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0329729c-1293-4c61-85e3-ccfa75fe2e25 · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.107037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84cdfbd8-5826-4501-9843-5d8d24bbc0fa · outbound

This paper cites Now, we define two sets, given anyη∈(0,1), S1 ={s∈S: 0<r ∗(s,πr∗(s))−max a∈A/πr∗(s) r∗ a(s)≤η}; S2 ={s∈S:r ∗(s,πr∗(s))−max a∈A/πr∗(s) r∗ a(s)>η}.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Now, we define two sets, given anyη∈(0,1), S1 ={s∈S: 0<r ∗(s,πr∗(s))−max a∈A/πr∗(s) r∗ a(s)≤η}; S2 ={s∈S:r ∗(s,πr∗(s))−max a∈A/πr∗(s) r∗ a(s)>η}

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:54.046769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a370af60-ad00-4d31-a9cb-6e3fc2fe9c40 · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.097126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d2ec498a-836a-462a-8c4b-ca0dec5f7998 · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.067826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation daf2ee32-c232-43a1-bfa2-15b3c775353e · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.057379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b0801597-7ea2-4518-b8f9-03d2e6f84c16 · outbound

This paper cites Without loss of generality, we also define classes of sub-networks ofFDNN, that is,{F1,F 2,···,F |A|}, with non-sharing hidden layers.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Without loss of generality, we also define classes of sub-networks ofFDNN, that is,{F1,F 2,···,F |A|}, with non-sharing hidden layers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:54.036104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 11e422ca-cc1a-43b4-9d64-cde127037e15 · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.026020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40a15220-f7fa-43f0-886a-384e0e4f9b36 · outbound

This paper cites an unresolved cited work.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:54.015633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e009a17-72c6-4c14-a707-f9738718d352 · outbound

This paper cites □ Different from the deep regression and classification problem, the convergence of the excess risk does not directly ensure the functional convergence of the estimated reward.

Learning Guarantee of Reward Modeling Using Deep Neural Networks □ Different from the deep regression and classification problem, the convergence of the excess risk does not directly ensure the functional convergence of the estimated reward

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:54.005167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5792b3a-01cc-4237-af5e-36dad5b3bfd8 · outbound

This paper cites and Bayati, M.

Learning Guarantee of Reward Modeling Using Deep Neural Networks and Bayati, M

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:53.875103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf49d02-47d6-4dca-a9cc-20311bf1a2e1 · outbound

This paper cites Dual Active Learning for Reinforcement Learning from Human Feedback.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Dual Active Learning for Reinforcement Learning from Human Feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:53.882277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a897fcd-bf4b-46a4-b36f-645274ee75c9 · outbound

This paper cites and Kupper, L.

Learning Guarantee of Reward Modeling Using Deep Neural Networks and Kupper, L

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:54.087748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff05ea61-5cf2-4448-bb54-cb05ad9c6d9f · outbound

This paper cites Secrets of RLHF in Large Language Models Part II: Reward Modeling.

Learning Guarantee of Reward Modeling Using Deep Neural Networks Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:53.893955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dca5193-da27-4c43-a9f9-60b89abe5e22 · outbound

This paper cites B., Balakrishnan, S., Bradley, J., Parekh, A., Ramch, K., Wainwright, M.

Learning Guarantee of Reward Modeling Using Deep Neural Networks B., Balakrishnan, S., Bradley, J., Parekh, A., Ramch, K., Wainwright, M

Reference 1897

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:54.077909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.