Paper Citation Record · LEDGER

Fine-Tuning Language Models with Advantage-Induced Policy Alignment

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2306.02231.

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

pith.paper-citation-record.v1
2306.02231 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:43:50.418201Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:53:16.384194Z

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 03c377c5-e3ab-436d-8210-6ccd562be404 · inbound

Thompson Sampling in Online RLHF with General Function Approximation cites this paper.

Thompson Sampling in Online RLHF with General Function Approximation Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:50.418201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d541982-153e-4321-8f10-3826e4d6b35f · inbound

Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards cites this paper.

Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T19:38:24.138569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d2ee59d-f3c4-464d-8a00-1d3a097ef619 · inbound

Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards cites this paper.

Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T06:47:16.922717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd3c7038-4d99-4149-9d54-430804dd56a6 · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:07:28.067831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50061053-d40d-49bc-bc12-ee3ff89a5d00 · inbound

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models cites this paper.

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.335886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf3a4c5a-57d2-4e9e-ad79-db6e2d1f44ec · inbound

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models cites this paper.

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:53:16.385564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0eb34602-8b01-4ae2-aec5-b8a4ddf6ede3 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 164

Resolution: unresolved; no resolver link, observed 2026-07-11T13:53:36.775836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 182af3d4-ac06-4c18-b103-917c2cb86351 · inbound

Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 165

Resolution: unresolved; no resolver link, observed 2026-08-02T08:40:51.240177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.