VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced
  Multimodal Reasoning
Reinforcement learning has proven its effectiveness in enhancing the
reasoning capabilities of large language models. Recent research efforts have
progressively extended this paradigm to multimodal reasoning tasks. Due to the
inherent complexity and diversity of multimodal tasks, especially in semantic
content and problem formulations, existing models often exhibit unstable
performance across various domains and difficulty levels. To address these
limitations, we propose VL-Cogito, an advanced multimodal reasoning model
trained via a novel multi-stage Progressive Curriculum Reinforcement Learning
(PCuRL) framework. PCuRL systematically guides the model through tasks of
gradually increasing difficulty, substantially improving its reasoning
abilities across diverse multimodal contexts. The framework introduces two key
innovations: (1) an online difficulty soft weighting mechanism, dynamically
adjusting training difficulty across successive RL training stages; and (2) a
dynamic length reward mechanism, which encourages the model to adaptively
regulate its reasoning path length according to task complexity, thus balancing
reasoning efficiency with correctness. Experimental evaluations demonstrate
that VL-Cogito consistently matches or surpasses existing reasoning-oriented
models across mainstream multimodal benchmarks spanning mathematics, science,
logic, and general understanding, validating the effectiveness of our approach.
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Ruifeng Yuan
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Chenghao Xiao
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chenghao xiao
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Sicong Leng
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Jianyu Wang
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Long Li
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jianghuyihei
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Weiwen Xu
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Xu Weiwen
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Hou Pong Chan
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Hou Pong (Ken) Chan
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kenchan0226
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Deli Zhao
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Tingyang Xu
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Zhongyu Wei
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Hao Zhang
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ZHANG HAO
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26hzhang
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Yu Rong
claimed_verified
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Yu Rong
user
Swrooy
VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced
  Multimodal Reasoning
Reinforcement learning has proven its effectiveness in enhancing the
reasoning capabilities of large language models. Recent research efforts have
progressively extended this paradigm to multimodal reasoning tasks. Due to the
inherent complexity and diversity of multimodal tasks, especially in semantic
content and problem formulations, existing models often exhibit unstable
performance across various domains and difficulty levels. To address these
limitations, we propose VL-Cogito, an advanced multimodal reasoning model
trained via a novel multi-stage Progressive Curriculum Reinforcement Learning
(PCuRL) framework. PCuRL systematically guides the model through tasks of
gradually increasing difficulty, substantially improving its reasoning
abilities across diverse multimodal contexts. The framework introduces two key
innovations: (1) an online difficulty soft weighting mechanism, dynamically
adjusting training difficulty across successive RL training stages; and (2) a
dynamic length reward mechanism, which encourages the model to adaptively
regulate its reasoning path length according to task complexity, thus balancing
reasoning efficiency with correctness. Experimental evaluations demonstrate
that VL-Cogito consistently matches or surpasses existing reasoning-oriented
models across mainstream multimodal benchmarks spanning mathematics, science,
logic, and general understanding, validating the effectiveness of our approach.
VL-Cogito, a multimodal reasoning model, uses a Progressive Curriculum Reinforcement Learning framework to improve performance across diverse tasks by dynamically adjusting difficulty and reasoning path length.
reinforcement learning multimodal reasoning Progressive Curriculum Reinforcement Learning PCuRL online difficulty soft weighting dynamic length reward mechanism