Paper Citation Record · LEDGER

Transforming and Combining Rewards for Aligning Large Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.00742.

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

pith.paper-citation-record.v1
2402.00742 v2

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 2026-08-18T06:34:40.430872+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:31:52.776300Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:47.876920Z

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 3fffca85-55af-4cda-a00a-bed68e84db18 · inbound

Seed-TTS: A Family of High-Quality Versatile Speech Generation Models cites this paper.

Seed-TTS: A Family of High-Quality Versatile Speech Generation Models Transforming and Combining Rewards for Aligning Large Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:26:37.399932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f4a72c2-4579-406e-9786-47ad96cedb5e · inbound

Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning cites this paper.

Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning Transforming and Combining Rewards for Aligning Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T04:37:43.697884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80e9000-c40a-4121-964b-61f1b9dc0c67 · inbound

Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models cites this paper.

Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models Transforming and Combining Rewards for Aligning Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T21:31:45.712536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab6a48ed-00b9-4874-a99c-bc7856e6a796 · inbound

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation cites this paper.

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation Transforming and Combining Rewards for Aligning Large Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T21:04:57.363613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4909cd43-a8e3-4144-8f5e-775542ef432e · inbound

Language Model Networks: Supervision-Efficient Learning through Dense Communication cites this paper.

Language Model Networks: Supervision-Efficient Learning through Dense Communication Transforming and Combining Rewards for Aligning Large Language Models

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:51:41.910711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 984a176d-4cc5-4aad-99dc-7fb317dde5bb · inbound

Language Model Networks: Supervision-Efficient Learning through Dense Communication cites this paper.

Language Model Networks: Supervision-Efficient Learning through Dense Communication Transforming and Combining Rewards for Aligning Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:52.776300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7c2a000-22cd-47a4-8c5e-76e767e818af · inbound

RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models Transforming and Combining Rewards for Aligning Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:06.839256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1378a620-b05f-49a2-b875-d8cf129227b0 · inbound

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective cites this paper.

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective Transforming and Combining Rewards for Aligning Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T06:06:24.003533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7406b8-da57-48b7-a5b5-fb213d6d1f82 · inbound

Reward Hacking in Rubric-Based Reinforcement Learning cites this paper.

Reward Hacking in Rubric-Based Reinforcement Learning Transforming and Combining Rewards for Aligning Large Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:12:14.083693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aec05c2e-5af9-45a5-9870-bee120dd3755 · inbound

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal cites this paper.

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal Transforming and Combining Rewards for Aligning Large Language Models

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.878366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.