Paper Citation Record · LEDGER

Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2503.12854.

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

pith.paper-citation-record.v1
2503.12854 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:50:38.778526Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:02:33.817155Z

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 beacce03-b4a2-4061-b7a1-b2e2731349e0 · inbound

Mathesis: Towards Formal Theorem Proving from Natural Languages cites this paper.

Mathesis: Towards Formal Theorem Proving from Natural Languages Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:50:38.778526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 450bebd0-af38-4ece-ac96-ebf204566c01 · inbound

Enhancing Large Language Models through Structured Reasoning cites this paper.

Enhancing Large Language Models through Structured Reasoning Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:59:47.417741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2d8a1c9-c418-4448-bdfa-652d2a27dfff · inbound

Sample-efficient LLM Optimization with Reset Replay cites this paper.

Sample-efficient LLM Optimization with Reset Replay Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T23:41:54.738087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 62801cd4-ce5a-4f1e-abfb-8c526f5297b7 · inbound

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning cites this paper.

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T09:33:40.888746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921189dd-55a7-47c7-8c07-88391aa3e3ff · inbound

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework cites this paper.

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T23:33:49.468251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae29fb2c-7c16-4ba5-b2d9-2e85298977f4 · inbound

Your Self-Play Algorithm is Secretly an Adversarial Imitator: Understanding LLM Self-Play through the Lens of Imitation Learning cites this paper.

Your Self-Play Algorithm is Secretly an Adversarial Imitator: Understanding LLM Self-Play through the Lens of Imitation Learning Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T05:49:25.124148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa1fc4f7-5128-4fc3-9ee6-e1f725596d84 · inbound

IRIS: Interpolative R\'enyi Iterative Self-play for Large Language Model Fine-Tuning cites this paper.

IRIS: Interpolative R\'enyi Iterative Self-play for Large Language Model Fine-Tuning Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:05.074027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 41a09afb-bd6c-4ae4-bfab-0ba069dbc723 · inbound

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models cites this paper.

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:16:25.953549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fd439143-50f9-432f-86e7-ec30bd916e06 · inbound

Confidence-Aware Alignment Makes Reasoning LLMs More Reliable cites this paper.

Confidence-Aware Alignment Makes Reasoning LLMs More Reliable Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:53.895403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c53353d-3041-44a5-9005-e0d8f5f1affb · inbound

How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR cites this paper.

How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:24:00.555652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 173be850-c0a6-47df-9987-16f2e2589fb1 · inbound

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization cites this paper.

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:45:17.528307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6ce62f4-2f0c-435d-9727-edc4d7977589 · inbound

Enhancing LLM Metacognition via Cognitive Pairwise Training cites this paper.

Enhancing LLM Metacognition via Cognitive Pairwise Training Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:02:33.820741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.