title: Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with
    Jigsaw Puzzles
summary: The application of rule-based reinforcement learning (RL) to multimodal large
  language models (MLLMs) introduces unique challenges and potential deviations
  from findings in text-only domains, particularly for perception-heavy tasks.
  This paper provides a comprehensive study of rule-based visual RL, using jigsaw
  puzzles as a structured experimental framework. Jigsaw puzzles offer inherent
  ground truth, adjustable difficulty, and demand complex decision-making, making
  them ideal for this study. Our research reveals several key findings:
  Firstly, we find that MLLMs, initially performing near to random
  guessing on the simplest jigsaw puzzles, achieve near-perfect accuracy and
  generalize to complex, unseen configurations through fine-tuning.
  Secondly, training on jigsaw puzzles can induce generalization to
  other visual tasks, with effectiveness tied to specific task configurations.
  Thirdly, MLLMs can learn and generalize with or without explicit
  reasoning, though open-source models often favor direct answering.
  Consequently, even when trained for step-by-step reasoning, they can ignore the
  thinking process in deriving the final answer. Fourthly, we observe
  that complex reasoning patterns appear to be pre-existing rather than emergent,
  with their frequency increasing alongside training and task difficulty.
  Finally, our results demonstrate that RL exhibits more effective
  generalization than Supervised Fine-Tuning (SFT), and an initial SFT cold start
  phase can hinder subsequent RL optimization. Although these observations are
  based on jigsaw puzzles and may vary across other visual tasks, this research
  contributes a valuable piece of jigsaw to the larger puzzle of collective
  understanding rule-based visual RL and its potential in multimodal learning.
  The code is available at: https://github.com/zifuwanggg/Jigsaw-R1.
paper_authors._id: 683e6b2d97fd742a8edb8a8e
paper_authors.name: Zifu Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T09:41:52.137Z
paper_authors._id: 64f5c7cb65a4b1acb20ffc15
paper_authors.avatarUrl: /avatars/67c42a6e26bbeddc4bf706eb8f1a75b1.svg
paper_authors.fullname: Zifu Wang
paper_authors.type: user
paper_authors.user: wangzifu
paper_authors._id: 683e6b2d97fd742a8edb8a8f
paper_authors.name: Junyi Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-03T09:42:00.703Z
paper_authors._id: 6477b2038ab7e732b6d8a9b5
paper_authors.avatarUrl: /avatars/c859a35b8965a904f103bbc34f36ab2a.svg
paper_authors.fullname: Junyi Zhu
paper_authors.type: user
paper_authors.user: RyanZhu
paper_authors._id: 683e6b2d97fd742a8edb8a90
paper_authors.name: Bo Tang
paper_authors._id: 683e6b2d97fd742a8edb8a91
paper_authors.name: Zhiyu Li
paper_authors._id: 683e6b2d97fd742a8edb8a92
paper_authors.name: Feiyu Xiong
paper_authors._id: 683e6b2d97fd742a8edb8a93
paper_authors.name: Jiaqian Yu
paper_authors._id: 683e6b2d97fd742a8edb8a94
paper_authors.name: Matthew B. Blaschko
paper_title: Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with
    Jigsaw Puzzles
paper_summary: The application of rule-based reinforcement learning (RL) to multimodal large
  language models (MLLMs) introduces unique challenges and potential deviations
  from findings in text-only domains, particularly for perception-heavy tasks.
  This paper provides a comprehensive study of rule-based visual RL, using jigsaw
  puzzles as a structured experimental framework. Jigsaw puzzles offer inherent
  ground truth, adjustable difficulty, and demand complex decision-making, making
  them ideal for this study. Our research reveals several key findings:
  Firstly, we find that MLLMs, initially performing near to random
  guessing on the simplest jigsaw puzzles, achieve near-perfect accuracy and
  generalize to complex, unseen configurations through fine-tuning.
  Secondly, training on jigsaw puzzles can induce generalization to
  other visual tasks, with effectiveness tied to specific task configurations.
  Thirdly, MLLMs can learn and generalize with or without explicit
  reasoning, though open-source models often favor direct answering.
  Consequently, even when trained for step-by-step reasoning, they can ignore the
  thinking process in deriving the final answer. Fourthly, we observe
  that complex reasoning patterns appear to be pre-existing rather than emergent,
  with their frequency increasing alongside training and task difficulty.
  Finally, our results demonstrate that RL exhibits more effective
  generalization than Supervised Fine-Tuning (SFT), and an initial SFT cold start
  phase can hinder subsequent RL optimization. Although these observations are
  based on jigsaw puzzles and may vary across other visual tasks, this research
  contributes a valuable piece of jigsaw to the larger puzzle of collective
  understanding rule-based visual RL and its potential in multimodal learning.
  The code is available at: https://github.com/zifuwanggg/Jigsaw-R1.
paper_ai_summary: Rule-based reinforcement learning applied to multimodal large language models demonstrates effective generalization in visual tasks, particularly using jigsaw puzzles, outperforming supervised fine-tuning.
paper_ai_keywords: rule-based reinforcement learning multimodal large language models visual RL jigsaw puzzles fine-tuning supervised fine-tuning complex decision-making visual tasks step-by-step reasoning generalization