Paper Citation Record · LEDGER

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

As of 17 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 5 inbound Pith citation observations for arXiv:2501.02790.

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

pith.paper-citation-record.v1
2501.02790 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:07:54.567013Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:59:20.189439Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:37:30.511368Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy8
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 52ba7fba-dc28-4045-8b69-1abab7b9054e · outbound

This paper cites Adding hops early in the boil (usually within the first 15 minutes) primarily contributes to the beer’s bitterness.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Adding hops early in the boil (usually within the first 15 minutes) primarily contributes to the beer’s bitterness

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.866830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4808866-602a-4262-885f-8183dba9a357 · outbound

This paper cites The bitterness level is moderate, and the hop flavors and some aromatic compounds are preserved better than in the early boil, thanks to the shorter exposure time.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model The bitterness level is moderate, and the hop flavors and some aromatic compounds are preserved better than in the early boil, thanks to the shorter exposure time

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.849886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8a7153d-d537-44ad-80cd-1803f0f73d12 · outbound

This paper cites This is because the shorter boiling time allows the volatile aromatic compounds to remain intact, while the alpha acids responsible for bitterness are less extracted.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model This is because the shorter boiling time allows the volatile aromatic compounds to remain intact, while the alpha acids responsible for bitterness are less extracted

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.832782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c53aa61d-2c2f-467d-aabd-d7ecc2e0ebb0 · outbound

This paper cites This is done after the primary fermentation has completed, and the beer is transferred to a secondary fermenter or directly to the bottle/keg.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model This is done after the primary fermentation has completed, and the beer is transferred to a secondary fermenter or directly to the bottle/keg

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.815952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c11a862a-8a5f-4fc8-b73e-49575bc40e86 · outbound

This paper cites Spinach and Feta Egg Muffins.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Spinach and Feta Egg Muffins

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.798773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e29cbc92-e1e1-44a7-bc90-a3cf0df62939 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.696961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae4b4585-9915-4c02-88a2-e5cdf1012f28 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.782680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c5d1c61f-8afa-473e-a930-88a2e9e7b443 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.764859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d217ee92-7bcc-44e6-909b-8d5c96c465e2 · outbound

This paper cites Stir until all the ingredients are evenly distributed.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Stir until all the ingredients are evenly distributed

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.747472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7003008-5cf3-4e7e-8df5-2692f73807c1 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.730212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d86e7d58-6d46-4edc-a883-1ca0aef06910 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.713758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 82ee1389-56bd-40b2-aa7b-54c17ac78e0c · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.680867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 351e48cd-4793-4374-84c4-bd526a8923d9 · outbound

This paper cites an unresolved cited work.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:07:54.663422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71260a24-a3bf-4002-af9f-1297cf6609bd · outbound

This paper cites The roots of the equation are: {roots[0]} and {roots[1]}.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model The roots of the equation are: {roots[0]} and {roots[1]}

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.646193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8176557-d097-4005-b745-46e67f6e0387 · outbound

This paper cites Global Statistics of All.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Global Statistics of All

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:07:54.628064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2eee8d5b-c2e1-402d-8d55-a6ea8292b7b7 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T22:07:54.481652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c7e0e76c-dfb0-42dd-a0ac-40850cb5cca2 · inbound

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning cites this paper.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:31.014205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87939929-964a-4e5a-88ea-1058304cd6c7 · inbound

TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization cites this paper.

TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:59:20.189439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5923323-4fcb-4b42-9458-093c65a4a7df · inbound

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges cites this paper.

Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:00:28.401522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 35917a05-ad3b-432d-881f-e8f9db0b75b6 · inbound

Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment cites this paper.

Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-09T23:04:18.030277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26de573c-73ff-40fa-a7b8-64a60a72c502 · inbound

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization cites this paper.

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reference 151

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:37:30.512754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.