Paper Citation Record · LEDGER

Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.15624.

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

pith.paper-citation-record.v1
2405.15624 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 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:54:17.348091Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:06:55.664941Z

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 883e9ec7-d15a-4f48-80bb-7fecda984673 · inbound

Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning cites this paper.

Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:54:17.348091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad8adb64-a226-4672-b9c4-5c220b28a170 · inbound

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models cites this paper.

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 130

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:46.127384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7dd4672-460b-4dea-824c-38c18e7fcd67 · inbound

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions cites this paper.

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 164

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:31.379025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299da6d6-f1b3-43da-9533-97f5226241fe · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.210425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c7807fa-509c-4bd1-bfec-804faf362c59 · inbound

Post-Training Large Language Models via Reinforcement Learning from Self-Feedback cites this paper.

Post-Training Large Language Models via Reinforcement Learning from Self-Feedback Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T12:17:54.036222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a389eaf3-3b2a-4e31-983e-67de858f2207 · inbound

rePIRL: Learn PRM with Inverse RL for LLM Reasoning cites this paper.

rePIRL: Learn PRM with Inverse RL for LLM Reasoning Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:14:10.940147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fccb803e-e0a8-48e8-8244-6ecc71d3f08f · inbound

rePIRL: Learn PRM with Inverse RL for LLM Reasoning cites this paper.

rePIRL: Learn PRM with Inverse RL for LLM Reasoning Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T03:33:44.742617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8296079b-4ebf-40a0-8935-3c49212b7bd7 · inbound

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models cites this paper.

SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:01:49.500518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef0db308-59a9-4f07-bc00-d28e1ad25aa1 · inbound

On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 107

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:36:08.632776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c50bd71b-63b7-49ab-83cd-53b4c0a0ccd8 · inbound

CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning cites this paper.

CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:06:55.666376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.