Paper Citation Record · LEDGER

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2404.03715.

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

pith.paper-citation-record.v1
2404.03715 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 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 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:33.077519Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:49:37.721117Z

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 ff11211d-bc73-4838-908e-26f990d76380 · inbound

KTO: Model Alignment as Prospect Theoretic Optimization cites this paper.

KTO: Model Alignment as Prospect Theoretic Optimization Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T12:17:53.564060Z

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 35f6eb9c-ac9b-4b3f-858d-507259d3fdbb · inbound

Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 90

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T12:04:10.572479Z

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 05481b0e-c6d2-4d15-9327-5f77272944cb · inbound

Process Reinforcement through Implicit Rewards cites this paper.

Process Reinforcement through Implicit Rewards Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:23:31.113118Z

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 3a9b81d3-fad4-400f-8a5b-9c988348fc5d · inbound

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 109

Resolution: unresolved; no resolver link, observed 2026-08-07T22:50:33.077519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9384154-a853-48d5-8e9e-5e44e46c2217 · inbound

Reinforcement Learning from Human Feedback cites this paper.

Reinforcement Learning from Human Feedback Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 205

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:32:01.386647Z

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 c8df08d7-fa7c-40af-90c7-3558c8025d87 · inbound

MPO: Multilingual Safety Alignment via Reward Gap Optimization cites this paper.

MPO: Multilingual Safety Alignment via Reward Gap Optimization Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:30.769929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf583308-af18-4836-b38e-fa24ff026969 · inbound

Learning a Pessimistic Reward Model in RLHF cites this paper.

Learning a Pessimistic Reward Model in RLHF Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:01:05.734257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c612a59-241f-4f9b-a974-7f75e4c5f031 · inbound

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:59.736916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b6b2cad-3767-4e73-9981-da4ba7c71a92 · inbound

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment cites this paper.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:44.872410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7f15eb2-1f01-4a41-b31c-5f4fe31cdcbc · inbound

Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function cites this paper.

Sign-SZPO: Provable Preference-based Reinforcement Learning with an Unknown Link Function Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:20:14.109193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fc2bec0-5495-40c1-a5e9-18cfb280fb3c · inbound

Debiasing Online Preference Learning via Preference Feature Preservation cites this paper.

Debiasing Online Preference Learning via Preference Feature Preservation Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:32.672178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bbf2b40-5962-4839-b7d1-094cdec1c94d · inbound

Multiplayer Nash Preference Optimization cites this paper.

Multiplayer Nash Preference Optimization Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.944650Z

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 375fda13-12d7-495a-ac33-4357f018d7f5 · inbound

Improved Bounds for Private and Robust Alignment cites this paper.

Improved Bounds for Private and Robust Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T13:42:06.117000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f59d078f-612b-4b29-96c8-2b6e0079f15a · inbound

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective cites this paper.

Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T06:06:23.730311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13f26489-4421-4416-a739-9397259589c6 · inbound

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy cites this paper.

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:01:49.584601Z

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 b200f40d-5da8-47c7-b62e-11bdda45921e · inbound

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability cites this paper.

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:56:30.887352Z

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 5d79cd9f-3635-4db2-96a7-9fdd7e45ab3b · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 154

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T04:57:17.354453Z

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 a65fb4dd-ff9b-4306-8680-9a32351a9c0b · inbound

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching cites this paper.

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 154

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:45:06.654565Z

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 454872d7-679a-46f8-baa3-63fe12fbeb02 · inbound

Common-agency Games for Multi-Objective Test-Time Alignment cites this paper.

Common-agency Games for Multi-Objective Test-Time Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 63

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T06:15:06.588171Z

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 160f2efb-8f9e-48a8-9cb4-0241ff246eda · inbound

Learning from Language Feedback via Variational Policy Distillation cites this paper.

Learning from Language Feedback via Variational Policy Distillation Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:39:00.287412Z

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 eed979e2-bcfc-4804-a09e-539e2e4fa576 · inbound

Transitivity Meets Cyclicity: Explicit Preference Decomposition for Dynamic Large Language Model Alignment cites this paper.

Transitivity Meets Cyclicity: Explicit Preference Decomposition for Dynamic Large Language Model Alignment Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:28:21.293961Z

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 39657248-29cf-426c-9954-0c4cfe4a1083 · inbound

S-SPPO: Semantic-Calibrated Self-Play Preference Optimization cites this paper.

S-SPPO: Semantic-Calibrated Self-Play Preference Optimization Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:56:19.406664Z

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 8a86bec4-31c5-4500-aca5-98e5479967fc · inbound

Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs cites this paper.

Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:46:33.121702Z

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 8af116e8-2e4a-4849-8af6-03779b44affc · inbound

AI Alignment From Social Choice Perspectives cites this paper.

AI Alignment From Social Choice Perspectives Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:49:37.723384Z

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.