Pith. sign in

Paper Citation Record · LEDGER

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

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:52:06.672777Z

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 b34079ab-483f-49e5-9763-b1af369a2c45 · inbound

Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment cites this paper.

Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T20:51:53.258371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:51:53.258371Z digest=sha256:fb6900b72bcc8be26cacd7792782a615556ce86e90a04d741cb96bf1f99873ee

Observation 94b18add-b7f0-402c-9aa4-b20a9a66d49f · inbound

SPRec: Self-Play to Debias LLM-based Recommendation cites this paper.

SPRec: Self-Play to Debias LLM-based Recommendation Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-11T17:17:41.304948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:17:41.304948Z digest=sha256:29c8d6f0843ede03e451fa1100fdb6fcd650b31fe30adbcd75bcc424fd5d364d

Observation a6d42416-3846-4696-b590-456ef9d30a64 · inbound

A Survey on Progress in LLM Alignment from the Perspective of Reward Design cites this paper.

A Survey on Progress in LLM Alignment from the Perspective of Reward Design Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-16T00:52:06.672777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:52:06.672777Z digest=sha256:be871a14f92bf834989f1d554555d44c182708839a434203958a87b86771d2d7

Observation 0410b6ec-8cc4-4b1e-8ad5-c9f8822da91d · inbound

Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs cites this paper.

Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T20:27:59.642465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:27:59.642465Z digest=sha256:cfd0022e110de59b1d595c9845f317f3de7b059f4e3758534af48079a17775d3

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.

source=arxiv_source observed=2026-08-07T14:54:17.348091Z digest=sha256:6f2dea685982316a66d5faa59c9250b97f3ec8fd85dd1fb1c9b0c810b60f76a4

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.

source=arxiv_source observed=2026-08-07T14:31:46.127384Z digest=sha256:7fd7fcd3973cc3ab8fb5a1801b8532f22ef3da0e48e68513a74c8a67a600db02

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.

source=pdf_text observed=2026-08-07T05:42:31.379025Z digest=sha256:55a6a768378b6debeddded335c2cf58750d1484b7283efec11d625850aaae73a

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.

source=pdf_text observed=2026-08-06T16:34:25.210425Z digest=sha256:8ea63a31dc8d105b4aa373c7b8776868fa74cedc1abdff14db0c7041b8d0711b

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.

source=arxiv_source observed=2026-08-06T12:17:54.036222Z digest=sha256:30bfa53258b42ced3af640a9cce71dfef03234a305692e6b2a128e5c4604c2ba

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-21T13:13:13.293921Z digest=sha256:31361464662021cde22acac4cf2cc32f856640beb4a0a12e3d4768b9bff1047d

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.

source=pdf_text observed=2026-08-03T03:33:44.742617Z digest=sha256:7cf4148a8cb8e0047e5560f715d50de45540d98f35cece581923a00a00d4632f

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-10T06:57:03.100519Z digest=sha256:f7bd3eccf13089fe338f3cb070e458972bfe2c5750994b46f24105666969c417

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-08T14:59:19.883399Z digest=sha256:fdf3424d51d48fd586697a93f29ce9df5786366f7ac1796ff128403f76e00e90

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-28T02:34:26.334078Z digest=sha256:e5c33170a5ab0edabb8f95547065b6a2ee10d7601660c4f5c0a520ac80963daf