Paper Citation Record · LEDGER

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

As of 8 August 2026, this Paper Citation Record lists 100 of 227 outbound references and 8 inbound Pith citation observations for arXiv:2505.18536.

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

pith.paper-citation-record.v1
2505.18536 v1

Coverage vector

measured 100 of 227 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:31:17.658024Z

measured 108 of 108 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T14:14:45.547957Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:50:10.284267Z

Reference resolution

100 of 227 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
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malformed identifier0
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External citation measurements

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Outbound references

Observation 40097270-201b-4671-98a9-96a86ad89f99 · outbound

This paper cites Reinforcement learning: An introduction, volume 1.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Reinforcement learning: An introduction, volume 1

Reference 1

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Observation cb105126-c838-4241-9d62-ac96dbab593a · outbound

This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Proximal Policy Optimization Algorithms

Reference 2

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Observation 92fa4f2c-49c8-4842-beb4-81fb763b8b74 · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Training language models to follow instructions with human feedback

Reference 3

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Observation dc18dca8-c084-486a-bde8-b13d88490781 · outbound

This paper cites GPT-4 Technical Report.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models GPT-4 Technical Report

Reference 4

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Observation b69da4cf-d149-4c1b-96e6-1f6d07b198ca · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 5

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Observation 3ae63e3d-924b-4ddf-9049-9c858c37329e · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 6

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Observation 81b9a938-15ac-413e-89a0-9c18cd2a4228 · outbound

This paper cites Introducing openai o1.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Introducing openai o1

Reference 7

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Observation 775fcef7-b5f1-4507-a9ea-236e8404c9b1 · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

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Observation e9572b1d-50da-4296-9599-ef43d6891972 · outbound

This paper cites Mind with Eyes: from Language Reasoning to Multimodal Reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Mind with Eyes: from Language Reasoning to Multimodal Reasoning

Reference 9

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Observation 05c33b26-e119-48be-820e-93c2b9a5bca1 · outbound

This paper cites Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models

Reference 10

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Observation 1649ca34-a81d-4b4d-9a27-1477797c16f6 · outbound

This paper cites A markovian decision process.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models A markovian decision process

Reference 11

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Observation c754e2d2-b518-4e9b-a3cc-22f8c68a7543 · outbound

This paper cites Convergence of q-learning: A simple proof.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Convergence of q-learning: A simple proof

Reference 12

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Observation b5172b4c-9916-4ecd-89e9-3b917ef718a9 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Playing Atari with Deep Reinforcement Learning

Reference 13

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Observation d94e0d25-b8ca-41a3-a2af-4be34aa47d87 · outbound

This paper cites Human-level control through deep reinforcement learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Human-level control through deep reinforcement learning

Reference 14

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Observation 245fef20-d355-49fc-8b71-b4eb0183c79e · outbound

This paper cites Deep reinforcement learning with double q-learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Deep reinforcement learning with double q-learning

Reference 15

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Observation 2a4e90db-6a84-411d-acba-9da6c3092262 · outbound

This paper cites Dueling network architectures for deep reinforcement learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Dueling network architectures for deep reinforcement learning

Reference 16

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Observation 549c1f6f-022e-4745-9135-a4973e29a50d · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Rainbow: Combining improvements in deep reinforcement learning

Reference 17

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Observation cf7111c2-ba27-468a-8ef9-5abf2e9bb760 · outbound

This paper cites Policy gradi- ent methods for reinforcement learning with function approximation.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Policy gradi- ent methods for reinforcement learning with function approximation

Reference 18

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Observation f9cc4bfa-6b53-4b20-84ba-52b228655f65 · outbound

This paper cites Actor-critic algorithms.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Actor-critic algorithms

Reference 19

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Observation e535d775-dcce-4472-9b7f-d40e5c2b0f2e · outbound

This paper cites Asynchronous methods for deep reinforce- ment learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Asynchronous methods for deep reinforce- ment learning

Reference 20

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Observation 97eef202-39fd-45c0-b930-3f4fc3ddc408 · outbound

This paper cites Trust region policy optimization.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Trust region policy optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:10.401052Z

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Observation e44ec016-602a-47a9-9e9b-e07d14cb0a49 · outbound

This paper cites LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Reference 22

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Observation 18a603a5-fbfa-4f22-9856-bbadb950af4b · outbound

This paper cites Reasoning with large language models, a survey.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Reasoning with large language models, a survey

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:10.596496Z

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Observation 8130aecb-d762-4666-9385-a6c66663eb02 · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 24

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Observation 2507e31d-74d1-4d0c-9646-f009e4797694 · outbound

This paper cites A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1

Reference 25

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Observation c7d0d5d5-e971-4419-b65e-6fb3f6c96c97 · outbound

This paper cites Thinking Machines: A Survey of LLM based Reasoning Strategies.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Thinking Machines: A Survey of LLM based Reasoning Strategies

Reference 26

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Observation 4be25c8b-00c7-4257-a608-0a4dfca4605f · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 27

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Observation 41b48aa1-d17b-4230-8db2-f10c5ecc337c · outbound

This paper cites 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models

Reference 28

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Observation f2629d68-8aff-47db-b431-dec3ae56af11 · outbound

This paper cites Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study

Reference 29

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Observation 7babe830-f6d3-4d4c-853a-38d3ad66ec9d · outbound

This paper cites Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey

Reference 30

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Observation 7b47eff1-c366-4880-b0c7-63af299acbcb · outbound

This paper cites Perception, reason, think, and plan: A survey on large multimodal reasoning models, 2025.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Perception, reason, think, and plan: A survey on large multimodal reasoning models, 2025

Reference 31

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Observation 1580e653-fd74-486c-9a03-a89e1b2efe30 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 32

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Observation 1c63fa0b-ff1e-47dc-ba9f-59a724e5c016 · outbound

This paper cites Introducing openai o3 and o4-mini.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Introducing openai o3 and o4-mini

Reference 33

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Observation 16623895-9b4e-4516-b4a9-2942d29a3356 · outbound

This paper cites Grok 3 beta — the age of reasoning agents.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Grok 3 beta — the age of reasoning agents

Reference 34

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Observation 86bcda32-3c84-4331-a4b3-9b6ad94135f3 · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

Reference 35

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Observation 5a962b26-e270-435c-9054-635f65b65d34 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 36

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Observation 8a89e05c-8fec-4938-b143-5e4d83dab744 · outbound

This paper cites What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret

Reference 37

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Observation cec5c169-3d82-4565-a809-74ad2bb3a543 · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 38

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Approximating kl divergence

Reference 39

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Observation d9a22cf9-07fd-4372-a117-a775ac9d9bc1 · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 40

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Observation 19d230f4-4c81-402d-850b-fcd968b02023 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Understanding R1-Zero-Like Training: A Critical Perspective

Reference 41

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Observation 58bcbb80-eed5-449a-9c08-cdb14c4ad7e0 · outbound

This paper cites Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond

Reference 42

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Observation fa8e1e95-c984-4d5d-9d37-e36825a8a940 · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Tinyzero

Reference 43

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Observation 061d8941-df23-4968-95e9-82c3a7d639e5 · outbound

This paper cites Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 44

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Observation 91ff927c-8585-4733-abf8-33827210c667 · outbound

This paper cites VisualPRM: An Effective Process Reward Model for Multimodal Reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models VisualPRM: An Effective Process Reward Model for Multimodal Reasoning

Reference 45

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Observation 051c8f17-2752-4dd2-86b8-fe78be8377d2 · outbound

This paper cites Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step

Reference 46

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Observation 65bc45ee-a926-45a0-a762-ba44de584247 · outbound

This paper cites Audio-reasoner: Improving reasoning capability in large audio language models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Audio-reasoner: Improving reasoning capability in large audio language models

Reference 47

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Observation 7b9ad463-7fbf-40ee-8a5f-8390c59237f0 · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering

Reference 48

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Observation 4da64ec5-3e68-4166-89e9-1a7e253e5cce · outbound

This paper cites SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning

Reference 49

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Observation 0e33bb8d-c9fa-47f6-aa5d-e271e2591963 · outbound

This paper cites R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning

Reference 50

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Observation bb8cd184-7364-4401-a920-23f1239dae01 · outbound

This paper cites EchoInk-R1: Exploring Audio-Visual Reasoning in Multimodal LLMs via Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models EchoInk-R1: Exploring Audio-Visual Reasoning in Multimodal LLMs via Reinforcement Learning

Reference 51

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Observation 5c1d1cfb-edff-4ec0-b4f5-2733b3adcbfc · outbound

This paper cites UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning

Reference 52

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Observation df3377d1-a140-4ef4-9cf3-330f7444868f · outbound

This paper cites GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents

Reference 53

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Observation 536638f2-8005-48a4-aa21-835ba8a79f81 · outbound

This paper cites InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners

Reference 54

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Observation 3f8fc103-ae94-46b0-a2ee-6afb7aa41df0 · outbound

This paper cites Metaspatial: Reinforcing 3d spatial reasoning in vlms for the metaverse.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Metaspatial: Reinforcing 3d spatial reasoning in vlms for the metaverse

Reference 55

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Observation 5bba1d4a-7bf3-4a4a-86c7-0cf2c789b572 · outbound

This paper cites Vagen: Training vlm agents with multi-turn reinforcement learning, 2025.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Vagen: Training vlm agents with multi-turn reinforcement learning, 2025

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:13.898943Z

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Observation f9b6a6df-454f-473b-a4f1-bc38cbc032f4 · outbound

This paper cites Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:13.950384Z

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Observation e804e79d-632a-4614-aa32-b12fa31e5490 · outbound

This paper cites Measuring multimodal mathematical reasoning with math-vision dataset.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Measuring multimodal mathematical reasoning with math-vision dataset

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:13.994703Z

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Observation 8ec617e7-3684-4a89-8b41-a39d5fd66cae · outbound

This paper cites Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.050530Z

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Observation 081cfcc6-85eb-496c-9b7f-2c9ed819fd1c · outbound

This paper cites MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.095636Z

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Observation 7e0cb03e-51e6-43a9-81be-841156b56272 · outbound

This paper cites Mm-math: Advancing multimodal math evaluation with process evaluation and fine-grained classification.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Mm-math: Advancing multimodal math evaluation with process evaluation and fine-grained classification

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.148793Z

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Observation cc5df7d0-5c0f-4aeb-b61c-29c32bf1862e · outbound

This paper cites We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.198563Z

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Observation a04b69c3-4337-40da-b1e5-a0d8569631fb · outbound

This paper cites Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.248813Z

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Observation 676d8967-aa9c-42ae-b9b5-db8c76bef781 · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.353463Z

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Observation b2a65190-299f-4f0e-b539-ec674f8f4100 · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.410946Z

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Observation e892892b-c10d-45a6-8065-9432d3feab2f · outbound

This paper cites Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.468895Z

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Observation 72cdbf10-5872-413f-8e80-172e4bc18ba5 · outbound

This paper cites Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.524790Z

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Observation 9ecc574c-ada4-42b0-9d5d-6131135f8e9a · outbound

This paper cites Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.568519Z

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Observation 1e676ee6-46e1-4876-99b2-0bbc10440dfa · outbound

This paper cites Virgo: A Preliminary Exploration on Reproducing o1-like MLLM.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Virgo: A Preliminary Exploration on Reproducing o1-like MLLM

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.613909Z

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Observation 9fa25072-aa78-4ace-88dd-3202fa3459b9 · outbound

This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.672507Z

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Observation 79d184a4-a0f3-43e3-a7be-ea742246e1a9 · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.730553Z

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Observation 5f5aa028-7c61-4799-ba77-a4ed484c335f · outbound

This paper cites LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.783221Z

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Observation afac0b4d-6a62-47bd-83f1-497cc0567c8d · outbound

This paper cites Mmr1: Ad- vancing the frontiers of multimodal reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Mmr1: Ad- vancing the frontiers of multimodal reasoning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.842854Z

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Observation 1f12bbf5-1ba4-436f-8aad-46720d7a2ad2 · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.922706Z

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Observation be1b0c93-c556-486c-8a10-b029c698a2e9 · outbound

This paper cites Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:14.986136Z

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Observation a457b09b-f3bc-4a6b-acf1-bffc6f8216c3 · outbound

This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.058067Z

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Observation e6ce94ea-7263-4968-aee8-9c4a75542481 · outbound

This paper cites OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.106642Z

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Observation fab00f3a-ea59-4597-8b1d-393e34eee0cd · outbound

This paper cites VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.147711Z

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Observation 5b345cae-a46b-427a-bb86-0ff66b6bf849 · outbound

This paper cites Noisyrollout: Reinforcing visual reasoning with data augmentation.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Noisyrollout: Reinforcing visual reasoning with data augmentation

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.223794Z

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Observation 00eac3aa-c4fa-40f9-ac6b-2a9297bbe228 · outbound

This paper cites Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.321786Z

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Observation 4a7a18cf-e7de-4faa-8a78-5e79f51dd4f3 · outbound

This paper cites Fast-slow thinking for large vision-language model reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Fast-slow thinking for large vision-language model reasoning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.448124Z

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Observation a1036505-1980-4395-87a2-f3dfba3bb0fd · outbound

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

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.565534Z

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Observation 56723f23-61f3-4feb-a363-1b8e79caf6f7 · outbound

This paper cites Crowdvlm-r1: Expanding r1 ability to vision language model for crowd counting using fuzzy group relative policy reward.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Crowdvlm-r1: Expanding r1 ability to vision language model for crowd counting using fuzzy group relative policy reward

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.674647Z

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Observation 570fc3a6-5555-469b-82cf-12e948096f05 · outbound

This paper cites R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.750218Z

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Observation 21a87c45-00b5-4cc3-8c36-faf3d4d6f4e7 · outbound

This paper cites Think or not think: A study of explicit thinking in rule-based visual reinforcement fine-tuning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Think or not think: A study of explicit thinking in rule-based visual reinforcement fine-tuning

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.837024Z

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Observation 0eff6400-1167-46f0-8655-a051a7ff64cf · outbound

This paper cites Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:15.937329Z

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Observation 496011d8-4e0b-4c11-b078-090c603f2b73 · outbound

This paper cites Q-Insight: Understanding Image Quality via Visual Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Q-Insight: Understanding Image Quality via Visual Reinforcement Learning

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.030204Z

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Observation 54ee01f6-ba4b-4c0a-bea4-e51e7d7f9665 · outbound

This paper cites Perception-R1: Pioneering Perception Policy with Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Perception-R1: Pioneering Perception Policy with Reinforcement Learning

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.116604Z

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Observation dcfc2b59-51ec-41ed-b2ca-a510e7d13a85 · outbound

This paper cites Compile Scene Graphs with Reinforcement Learning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Compile Scene Graphs with Reinforcement Learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.244853Z

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Observation 6fe23011-cb10-4f82-b0f2-5d19a96a19ce · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Relation-r1: Cognitive chain-of-thought guided reinforcement learning for unified relational comprehension

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.408178Z

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Observation 1eee2c39-18e0-4c37-8fa5-68c866112ea6 · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-track: Direct application of mllms to visual object tracking via reinforcement learning

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.533191Z

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Observation 5cf62a27-321c-471b-b20c-074f2896c833 · outbound

This paper cites SeekWorld: Geolocation is a natural RL task for o3-like visual clue-tracking.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models SeekWorld: Geolocation is a natural RL task for o3-like visual clue-tracking

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.665298Z

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Observation 8533d11d-b34c-4e6c-9ea3-fe6cdb35c3f8 · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.855930Z

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Observation b86b18bf-b91f-452c-bf36-547c9fa8a0c5 · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:16.974601Z

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Observation fa6ac10c-57da-444c-8232-5b6303355bab · outbound

This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.115856Z

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Observation e61f2bfb-3c1d-4de3-9c5c-a0b9edd41418 · outbound

This paper cites Reason-rft: Reinforcement fine-tuning for visual reasoning.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Reason-rft: Reinforcement fine-tuning for visual reasoning

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.206331Z

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Observation 967f09ed-0582-4c9f-8691-c1c707ee491e · outbound

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Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.317950Z

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Observation ae5e1ace-dbc9-410d-9784-e1158d796821 · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.425233Z

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Observation ea82e587-f15f-457d-b9f4-c78629abcb86 · outbound

This paper cites Kimi-VL Technical Report.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models Kimi-VL Technical Report

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.543542Z

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Observation 5214968e-aa2d-443b-8d4e-ed8998ede8dd · outbound

This paper cites R1-vision: Let’s first take a look at the image.

Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models R1-vision: Let’s first take a look at the image

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.658024Z

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Pith citing papers

Observation 9161386b-c4f9-478d-afcf-2bc15d5c4f0c · inbound

Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization cites this paper.

Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T19:50:33.810618Z

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Observation 5c9dc37b-4062-4914-bf2e-5acfc1754921 · inbound

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LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:31:32.085860Z

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Observation cc3102e1-4c8c-419d-be8b-9b6a5b0169e9 · inbound

OneThinker: All-in-one Reasoning Model for Image and Video cites this paper.

OneThinker: All-in-one Reasoning Model for Image and Video Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:11:26.532560Z

Source-reported events for the cited work

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Observation 7fac40b1-5516-4982-9ade-633169de5a16 · inbound

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CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:02:42.575958Z

Source-reported events for the cited work

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Observation df4417a6-4e26-4106-8a73-5ae38a3056af · inbound

OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks cites this paper.

OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:30:58.604506Z

Source-reported events for the cited work

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Observation 42e34c38-0cf8-4028-9238-765ca4e6a02b · inbound

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Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:29.388556Z

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Observation 950009ec-c769-48be-929b-79169082bc1a · inbound

Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training cites this paper.

Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:49:36.967214Z

Source-reported events for the cited work

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Observation 91d4c9ac-a3cf-4d38-9d23-bef70906eb8f · inbound

SSMNBench: Diagnosing Image-based Cross-View Human-Object Understanding via Single-View Sufficiency and Multi-View Necessity cites this paper.

SSMNBench: Diagnosing Image-based Cross-View Human-Object Understanding via Single-View Sufficiency and Multi-View Necessity Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:10.285898Z

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.