Paper Citation Record · LEDGER

Multiplayer Nash Preference Optimization

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2509.23102.

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

pith.paper-citation-record.v1
2509.23102 v3

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T13:09:54.433720Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T14:26:06.428076Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-20T14:28:21.295190Z

Reference resolution

48 of 48 outbound references displayed

verified exact38
verified fuzzy6
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 990ce93d-a989-401a-888f-8910532dc3fb · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Multiplayer Nash Preference Optimization Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.010757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90faa925-f9ee-4c16-8373-4b82a5ff6928 · outbound

This paper cites Human Alignment of Large Language Models through Online Preference Optimisation.

Multiplayer Nash Preference Optimization Human Alignment of Large Language Models through Online Preference Optimisation

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.031523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87a54057-82c6-411a-95d6-f5c709ca948e · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Multiplayer Nash Preference Optimization Evaluating Large Language Models Trained on Code

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.992454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f265f3d-330e-4ab6-b074-ad16d690f936 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Multiplayer Nash Preference Optimization Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.996253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a484db74-b16e-4104-8868-e16e157377c1 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Multiplayer Nash Preference Optimization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.969722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 599039fa-d35e-4424-951b-8c5c78476c6a · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

Multiplayer Nash Preference Optimization UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.049816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f28c62cc-cde8-4b72-b882-e0e16d0f2cd3 · outbound

This paper cites RLHF Workflow: From Reward Modeling to Online RLHF.

Multiplayer Nash Preference Optimization RLHF Workflow: From Reward Modeling to Online RLHF

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.060389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b6d2d59-44a1-493b-a62d-710e3aeb177d · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Multiplayer Nash Preference Optimization Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.046566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c017d731-c26d-4c09-a187-5bde5969b007 · outbound

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

Multiplayer Nash Preference Optimization KTO: Model Alignment as Prospect Theoretic Optimization

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.057089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 76f6a24b-e98e-4788-ac89-2b54f12811da · outbound

This paper cites Robust Preference Optimization through Reward Model Distillation.

Multiplayer Nash Preference Optimization Robust Preference Optimization through Reward Model Distillation

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.953881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e4e2ea0-d071-4431-9fb4-0fad811c067d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Multiplayer Nash Preference Optimization DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.018070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e318a42-e6b4-4224-8dd0-dd1bc80105a0 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Multiplayer Nash Preference Optimization Measuring Massive Multitask Language Understanding

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.035030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f66dc85-7e8a-49d5-8fe7-94bcc5b8e78d · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

Multiplayer Nash Preference Optimization ORPO: Monolithic Preference Optimization without Reference Model

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.071919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a53c05b3-003c-453e-9298-06c4da0769c6 · outbound

This paper cites From live data to high-quality benchmarks: The arena-hard pipeline.Blog post.[Accessed 07-02-2025].

Multiplayer Nash Preference Optimization From live data to high-quality benchmarks: The arena-hard pipeline.Blog post.[Accessed 07-02-2025]

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:25.001298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0315044d-b440-40a0-9fd4-df3c56a3b16a · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Multiplayer Nash Preference Optimization TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.091395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8ab4fa3-0eda-43c9-b095-3099760038b8 · outbound

This paper cites Decoupled Weight Decay Regularization.

Multiplayer Nash Preference Optimization Decoupled Weight Decay Regularization

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.987973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 59e7694c-76f2-4396-a14c-70c57178ce24 · outbound

This paper cites The hidden link between rlhf and contrastive learning.arXiv preprint arXiv:2506.22578.

Multiplayer Nash Preference Optimization The hidden link between rlhf and contrastive learning.arXiv preprint arXiv:2506.22578

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.949194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60b45b80-0383-4fa7-8acb-c871fe844295 · outbound

This paper cites Nash Learning from Human Feedback.

Multiplayer Nash Preference Optimization Nash Learning from Human Feedback

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:11:23.958548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f2724e5-37d9-45c3-8e8f-0be4bf8d592e · outbound

This paper cites Pre-dpo: Improving data utilization in direct preference optimization using a guiding reference model.arXiv preprint arXiv:2504.15843.

Multiplayer Nash Preference Optimization Pre-dpo: Improving data utilization in direct preference optimization using a guiding reference model.arXiv preprint arXiv:2504.15843

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.098915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a85e76b-341b-4052-832a-3d4680e1b659 · outbound

This paper cites Disentangling Length from Quality in Direct Preference Optimization.

Multiplayer Nash Preference Optimization Disentangling Length from Quality in Direct Preference Optimization

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.001156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

This paper cites Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences.

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 2026-08-07T06:34:17.273281+00:00.

Observation d5cea3a4-c3cd-443b-96d8-cc17c2d6d8c4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multiplayer Nash Preference Optimization Proximal Policy Optimization Algorithms

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.939607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40c2dd33-6f91-4f15-9b98-a6e8e4c06910 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Multiplayer Nash Preference Optimization DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:23.962835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b233b10-28b9-47d9-b0cd-ab49eb3aba8d · outbound

This paper cites A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games.

Multiplayer Nash Preference Optimization A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.978594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a01f68e3-aa5b-46ee-b15d-5caafd728814 · outbound

This paper cites Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment.

Multiplayer Nash Preference Optimization Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.076019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e404c24-35ac-48b4-8b18-4832b6d28ae9 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Multiplayer Nash Preference Optimization Gemini: A Family of Highly Capable Multimodal Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.079371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5128dbfc-81aa-47b3-8f2c-44cc02513757 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Multiplayer Nash Preference Optimization Gemma 2: Improving Open Language Models at a Practical Size

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.083212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d30fb4d1-e33d-41f5-81fb-75d86b60e1bd · outbound

This paper cites Causal Confusion and Reward Misidentification in Preference-Based Reward Learning.

Multiplayer Nash Preference Optimization Causal Confusion and Reward Misidentification in Preference-Based Reward Learning

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:11:24.068249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 16949879-736a-40a2-b1a9-74e0f343206d · outbound

This paper cites Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints.

Multiplayer Nash Preference Optimization Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.039547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e630a4a0-faaa-45a8-8123-a41233ba089d · outbound

This paper cites Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment.

Multiplayer Nash Preference Optimization Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.006252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c069f4b2-bd1d-4910-bd7c-1ac1976f5b5e · outbound

This paper cites Self-Play Preference Optimization for Language Model Alignment.

Multiplayer Nash Preference Optimization Self-Play Preference Optimization for Language Model Alignment

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T13:11:24.087385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cc1d093a-2bae-44c8-a20e-caad02b45eec · outbound

This paper cites Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.

Multiplayer Nash Preference Optimization Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.053907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a187e3c4-ed2a-4fd9-bc9c-25ad2c792f07 · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Multiplayer Nash Preference Optimization Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.027237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ea2b1167-7325-4b3d-a3e5-78e03367dc93 · outbound

This paper cites Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

Multiplayer Nash Preference Optimization Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.983816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2519bbae-2272-4422-9df6-d6745d1c3c19 · outbound

This paper cites Qwen3 Technical Report.

Multiplayer Nash Preference Optimization Qwen3 Technical Report

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.108463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d2ebbe0-42e6-41b1-a165-1904522704bc · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Multiplayer Nash Preference Optimization DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.043054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b517741-e3b3-42c5-a97d-37e8906b71d3 · outbound

This paper cites VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks.

Multiplayer Nash Preference Optimization VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.064514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e24e9a2-3a0e-460d-bb4d-1c0251ef9412 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Multiplayer Nash Preference Optimization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.014337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8df3fe8-36b7-4734-82a2-68f7d0244696 · outbound

This paper cites Improving LLM General Preference Alignment via Optimistic Online Mirror Descent.

Multiplayer Nash Preference Optimization Improving LLM General Preference Alignment via Optimistic Online Mirror Descent

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.022728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da2b461c-efe9-45b4-95bb-25728d5e8ff4 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Multiplayer Nash Preference Optimization Instruction-Following Evaluation for Large Language Models

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.094783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95a1eeb1-752b-4012-a402-27bab3384241 · outbound

This paper cites Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback.

Multiplayer Nash Preference Optimization Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:24.103703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c95817d-50dd-43a6-b6c0-f3a4f254a0ed · outbound

This paper cites WPO: Enhancing RLHF with Weighted Preference Optimization.

Multiplayer Nash Preference Optimization WPO: Enhancing RLHF with Weighted Preference Optimization

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:11:23.974150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d4d6ceb-0672-4a88-89dc-d9c1badf1550 · outbound

This paper cites Self-play methods like SPIN (Chen et al., 2024), SPPO (Wu et al., 2024), and INPO (Zhang et al., 2025b) use no- regret dynamics, while Pre-DPO (Pan et al.

Multiplayer Nash Preference Optimization Self-play methods like SPIN (Chen et al., 2024), SPPO (Wu et al., 2024), and INPO (Zhang et al., 2025b) use no- regret dynamics, while Pre-DPO (Pan et al

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:24.993887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a72b05d9-8af9-4b55-b7a2-d928e8501565 · outbound

This paper cites More recent methods, such as ONPO (Zhang et al., 2025a) and EGPO (Zhou et al., 2025), introduce optimism and extragradient techniques for stable convergence under noisy preferences.

Multiplayer Nash Preference Optimization More recent methods, such as ONPO (Zhang et al., 2025a) and EGPO (Zhou et al., 2025), introduce optimism and extragradient techniques for stable convergence under noisy preferences

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:24.996355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f46fac3-4fcb-466e-bc98-8f3d9eb3a37b · outbound

This paper cites INPO (Zhang et al., 2025b) is reproduced according to the settings described in the paper.

Multiplayer Nash Preference Optimization INPO (Zhang et al., 2025b) is reproduced according to the settings described in the paper

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:24.991274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c7501f8-5662-4941-ab8a-a1d0eefd0b6e · outbound

This paper cites minimal.

Multiplayer Nash Preference Optimization minimal

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:24.988894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e66d95a-469f-4e4b-a4b8-32ce090948d0 · outbound

This paper cites logπθ y+t π′t y+t −logπθ y−t π′t y−t −η 2 #2 , whereπ′t= argminπ E(x,y+t ,y−t)∼Dt.

Multiplayer Nash Preference Optimization logπθ y+t π′t y+t −logπθ y−t π′t y−t −η 2 #2 , whereπ′t= argminπ E(x,y+t ,y−t)∼Dt

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T13:11:24.986614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c4ff70f-d39e-4c71-b097-6b5b5a848949 · outbound

This paper cites an unresolved cited work.

Multiplayer Nash Preference Optimization Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-05-18T13:11:24.998801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 069db180-de2f-4c4e-a638-63ad38bbb031 · inbound

Towards General Preference Alignment: Diffusion Models at Nash Equilibrium cites this paper.

Towards General Preference Alignment: Diffusion Models at Nash Equilibrium Multiplayer Nash Preference Optimization

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-11T17:56:06.889210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1091f69-54be-48f8-bfb9-9b2a54ebea9a · 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 Multiplayer Nash Preference Optimization

Reference 101

Resolution: verified exact; local_arxiv, observed 2026-05-20T14:28:21.296641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.