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Paper Citation Record · LEDGER

Fine-Tuning Language Models with Advantage-Induced Policy Alignment

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 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 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:13:35.124526Z

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 58f29db0-07ed-4283-88e8-8a00c4d0fe53 · inbound

BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment cites this paper.

BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T19:14:37.109155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T19:14:37.109155Z digest=sha256:b1e77e13d081f2f7ea6042c56e9f8020f197ea5974420105fa5d4c90a185b29d

Observation 84b5bfc4-f444-45dd-ac05-f508bab73996 · inbound

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment cites this paper.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T12:16:31.947063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:16:31.947063Z digest=sha256:56513ca4d49f3cd45a098c1f3d27c8cc60b55f2359191aabf57fe8113eec1d0e

Observation d85b7d3b-7abd-403b-b6bb-e5e2c83b44f6 · inbound

EfficientLLM: Efficiency in Large Language Models cites this paper.

EfficientLLM: Efficiency in Large Language Models Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:35.124526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:13:35.124526Z digest=sha256:2f7cdfa43cba2454722df7b8455700162cc656f638c51cabe60bb71fcd7f3af5

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.

source=pdf_text observed=2026-08-07T12:43:50.418201Z digest=sha256:b831cd7b1ffab8c14b52a714c83c4dffcd039507005a565a1faec4fc1368c910

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.

source=pdf_text observed=2026-08-03T19:38:24.138569Z digest=sha256:404ce3c96666fb3294394e9313e094f9e9c65ba1b9fbfe5ff0ae60036bd23e96

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.

source=pdf_text observed=2026-08-04T06:47:16.922717Z digest=sha256:dcb94ce05cee9b0ef034d60283fbd1168b875b7612ff5f27685f37478978c70a

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T07:03:00.503644Z digest=sha256:8da23a720b03d8108539f23305145455fdf2435edd34993441c2a26260ad91fc

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-14T21:06:01.667173Z digest=sha256:850326158f533a6f56931c9e3d60edde8aafa210c14648add631e3a1e6db1de4

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-29T08:44:53.969301Z digest=sha256:6a7934ddaf14c6db5adb15b3e2b3364b1601c3c811f2a35ca2c0084cbfff4fc1

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.

source=arxiv_source observed=2026-07-11T13:53:36.775836Z digest=sha256:05d4ac43a2cdf126245ecb4fa883cff253b6d1bab2d76eb2f20d1ac10a460bf8

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.

source=arxiv_source observed=2026-08-02T08:40:51.240177Z digest=sha256:d8a5c1c21945fc7e2d678bbaaefbbcb0e76e2173c6fbd588cdf832c7a02b742b