Paper Citation Record · LEDGER

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 5 inbound Pith citation observations for arXiv:2505.24871.

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

pith.paper-citation-record.v1
2505.24871 v2

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:19:33.840820Z

measured 76 of 76 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:15:52.772665Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:24:00.434533Z

Reference resolution

71 of 71 outbound references displayed

verified exact0
verified fuzzy7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2dc90553-8ba9-43d5-a898-a69e774daed7 · outbound

This paper cites Alayrac, J.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Alayrac, J

Reference 1

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Observation f3e214dd-d14f-4ad1-af5b-4b898e03b140 · outbound

This paper cites Efficient Online Data Mixing For Language Model Pre-Training.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Efficient Online Data Mixing For Language Model Pre-Training

Reference 2

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Source-reported events for the cited work

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Observation 00e694f5-71e2-49b2-aafa-6c45738c459f · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Constitutional AI: Harmlessness from AI Feedback

Reference 3

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Source-reported events for the cited work

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Observation 1b1e10be-3f0d-4fff-bd10-b63f1a4eb349 · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 5

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Observation 0693db7d-7433-4d89-9fec-4b76efbc542e · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 6

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Observation 9fab8f8f-2d3a-4d91-b582-ce7504e130dc · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 7

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Source-reported events for the cited work

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Observation 09960a24-cca9-4205-b572-34c56db39c0a · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 4f4c7406-6758-4888-825c-15e77db8b3cf · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:19:39.796438Z

Source-reported events for the cited work

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Observation 2344a5a9-f500-4012-b701-1bcb8274e308 · outbound

This paper cites UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining

Reference 10

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Observation 2b9990b6-8dc9-4bee-a015-bc7eb4ced88f · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.ArXiv Preprint, 2025.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.ArXiv Preprint, 2025

Reference 11

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Observation 79346d66-c34d-4823-8d39-2c09636ce64d · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:19:39.507566Z

Source-reported events for the cited work

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Observation 6ea1a21b-84b2-436f-ba79-57296eaa9c93 · outbound

This paper cites Devlin, M.-W.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Devlin, M.-W

Reference 13

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 14

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Observation e135594d-1c46-4d77-8a61-9b2fe77ab4d0 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Teaching Large Language Models to Reason with Reinforcement Learning

Reference 17

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Observation 37c2ea2a-7d24-4ba1-9296-5a561771ff43 · outbound

This paper cites Optimizing Pretraining Data Mixtures with LLM-Estimated Utility.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Optimizing Pretraining Data Mixtures with LLM-Estimated Utility

Reference 18

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Observation ca94893b-7245-4457-820f-027ef55c824e · outbound

This paper cites Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 19

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Observation e4778dbe-e24c-4b26-8d07-f77022815ca7 · outbound

This paper cites Datamodels: Predicting Predictions from Training Data.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Datamodels: Predicting Predictions from Training Data

Reference 20

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Observation 7fec577c-30d3-4dca-bc9d-0105e7d12de0 · outbound

This paper cites Kazemnejad, M.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Kazemnejad, M

Reference 21

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Observation cc549ef2-8849-4736-990d-a8cc6a122214 · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 22

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Observation 454816c9-254c-4c12-9dff-b963bfc79b26 · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 23

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Observation 1cccf3fd-6ce4-423e-8ea0-56de82d54d1e · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 24

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 25

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Observation 0509d813-4b14-4f76-9936-5fb14ced841a · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 26

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Observation c6b2d7ee-8903-4e5f-800a-4a7f606b1c26 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 27

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Observation 3306c2d2-a036-43da-9e03-02a335611353 · outbound

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

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 28

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Observation 63641a10-1a9d-4b77-b9a8-2555350f9de6 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 29

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Observation ebbcf531-7dbb-4c0c-ac47-90e97aff91e5 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 30

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Observation 1d85ef55-57af-4a92-a40e-93f2e3046e45 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 31

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Observation 7fb0ce7a-3ae3-4735-a501-a108e128d0cb · outbound

This paper cites Visual Instruction Tuning.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Visual Instruction Tuning

Reference 32

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Observation 102a6ad2-1187-4852-a81b-9834676a21ac · outbound

This paper cites RegMix: Data Mixture as Regression for Language Model Pre-training.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning RegMix: Data Mixture as Regression for Language Model Pre-training

Reference 33

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Observation dd864dc0-3ecf-4ea1-9b0e-2e11bbfa8ceb · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 34

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Observation 0c60ca46-2971-49c5-948b-02858424b992 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 35

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Observation b2c552c9-0fc2-461c-b13e-6cafda9e26f9 · outbound

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Reference 36

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Observation 63d2c6ca-82ff-491b-9fac-58dc5be8544b · outbound

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Reference 37

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Observation 98ec5f18-7a3f-447c-8cad-a5c5a083d1b3 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 38

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Observation 69ff9057-a129-4833-a766-311e787d8227 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 39

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Observation 4db49e7b-67f9-4fcc-924e-d1eeb347f82e · outbound

This paper cites ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

Reference 40

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Observation 0d2b4e5c-f726-46dd-9e54-1c92dd1fc29a · outbound

This paper cites Mathew, V.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Mathew, V

Reference 41

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Observation 07b05db5-4d96-4a24-883d-5afaf2c774c6 · outbound

This paper cites McKinzie, Z.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning McKinzie, Z

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:36.939924Z

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Observation 506e6805-bec1-42ef-8741-9021d8de2142 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 43

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 44

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Observation 1668b6b2-9509-45c9-b2d5-cbb91bfdbbfd · outbound

This paper cites Cosmos-reason1: From physical common sense to embodied reasoning.ArXiv Preprint, 2025.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Cosmos-reason1: From physical common sense to embodied reasoning.ArXiv Preprint, 2025

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:36.771630Z

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Observation 36adb5f2-e95f-483b-895a-81d9efcd6413 · outbound

This paper cites GPT-4 Technical Report.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning GPT-4 Technical Report

Reference 46

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Observation b2b5241e-a48c-4c92-bda0-a3b0c03496aa · outbound

This paper cites Training language models to follow instructions with human feedback.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Training language models to follow instructions with human feedback

Reference 47

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Observation 4e7f3f0e-9d5a-45c9-8842-d72f6ed6f0ca · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Ouyang, J

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:36.611155Z

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Observation 05f165ac-e7f8-4847-bc0f-9120a054e969 · outbound

This paper cites Peng, Chris, X.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Peng, Chris, X

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:36.437611Z

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Observation 1d5b4da2-473d-4f1a-90e3-d6c1dc18299a · outbound

This paper cites Radford, J.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Radford, J

Reference 50

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Observation cbc401ff-1088-4f68-a918-586d04aee518 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 51

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Observation dd62f111-703f-48e8-8adc-cfc620dd31ba · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:19:36.227187Z

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Observation befcf928-f64e-4cd1-a5d8-aeab00ba624a · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 53

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Observation 9f92bc84-026a-4511-8e3a-6714df7829dd · outbound

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

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T12:19:32.007792Z

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Observation 5ef0fab3-046a-4843-8f4d-86c506c1ef34 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 55

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Observation 367cc09a-05af-44dc-b842-ae928ee91792 · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 56

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Observation be5e165c-dd85-497e-bbf7-d2e9b81ce38d · outbound

This paper cites an unresolved cited work.

MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:19:36.088926Z

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 58

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Reference 64

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Self-rewarding correction for mathematical reasoning

Reference 65

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Reference 66

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Reference 67

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Reference 68

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Reference 69

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Reference 70

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Observation 180fd3ef-1eda-4c11-8770-d40abbb02e9f · outbound

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Reference 71

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Reference 72

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Observation 743b0234-2b6f-4c89-a45c-ca3607796007 · outbound

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

Reference 73

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MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning aha moment

Reference 74

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Pith citing papers

Observation bb4826de-a215-4f05-b0f8-2f7d09409a9e · inbound

Perception-Aware Policy Optimization for Multimodal Reasoning cites this paper.

Perception-Aware Policy Optimization for Multimodal Reasoning MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

Reference 11

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Observation 2917cdcd-d538-4c7a-851d-80e5c0bc0eed · inbound

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models cites this paper.

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

Reference 53

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Multi-Task GRPO: Reliable LLM Reasoning Across Tasks cites this paper.

Multi-Task GRPO: Reliable LLM Reasoning Across Tasks MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

Reference 71

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Observation 2305ebed-33fd-4d4f-bbe1-e088f0d8af4c · inbound

Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models cites this paper.

Harmony in Diversity: Multi-domain Contrastive Policy Optimization for Large Reasoning Models MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

Reference 18

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Observation 5caa1d1d-90d8-4143-9426-0394621fd17e · inbound

Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning cites this paper.

Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning

Reference 21

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