SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward
Recent advances have shown success in eliciting strong reasoning abilities in
multimodal large language models (MLLMs) through rule-based reinforcement
learning (RL) with outcome rewards. However, this paradigm typically lacks
supervision over the thinking process leading to the final outcome.As a result,
the model may learn sub-optimal reasoning strategies, which can hinder its
generalization ability. In light of this, we propose SophiaVL-R1, as an attempt
to add reward signals for the thinking process in this paradigm. To achieve
this, we first train a thinking reward model that evaluates the quality of the
entire thinking process. Given that the thinking reward may be unreliable for
certain samples due to reward hacking, we propose the Trust-GRPO method, which
assigns a trustworthiness weight to the thinking reward during training. This
weight is computed based on the thinking reward comparison of responses leading
to correct answers versus incorrect answers, helping to mitigate the impact of
potentially unreliable thinking rewards. Moreover, we design an annealing
training strategy that gradually reduces the thinking reward over time,
allowing the model to rely more on the accurate rule-based outcome reward in
later training stages. Experiments show that our SophiaVL-R1 surpasses a series
of reasoning MLLMs on various benchmarks (e.g., MathVisita, MMMU),
demonstrating strong reasoning and generalization capabilities. Notably, our
SophiaVL-R1-7B even outperforms LLaVA-OneVision-72B on most benchmarks, despite
the latter having 10 times more parameters. All code, models, and datasets are
made publicly available at https://github.com/kxfan2002/SophiaVL-R1.
682fe2b865bac3ec3556c016
Kaixuan Fan
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2025-05-26T08:16:24.518Z
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kxbunny
user
bunny127
682fe2b865bac3ec3556c017
Kaituo Feng
claimed_verified
2025-05-27T12:13:41.509Z
67079840a9bcb7459b8d2a46
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Kaituo Feng
user
KaituoFeng
682fe2b865bac3ec3556c018
Haoming Lyu
682fe2b865bac3ec3556c019
Dongzhan Zhou
682fe2b865bac3ec3556c01a
Xiangyu Yue
SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward
Recent advances have shown success in eliciting strong reasoning abilities in
multimodal large language models (MLLMs) through rule-based reinforcement
learning (RL) with outcome rewards. However, this paradigm typically lacks
supervision over the thinking process leading to the final outcome.As a result,
the model may learn sub-optimal reasoning strategies, which can hinder its
generalization ability. In light of this, we propose SophiaVL-R1, as an attempt
to add reward signals for the thinking process in this paradigm. To achieve
this, we first train a thinking reward model that evaluates the quality of the
entire thinking process. Given that the thinking reward may be unreliable for
certain samples due to reward hacking, we propose the Trust-GRPO method, which
assigns a trustworthiness weight to the thinking reward during training. This
weight is computed based on the thinking reward comparison of responses leading
to correct answers versus incorrect answers, helping to mitigate the impact of
potentially unreliable thinking rewards. Moreover, we design an annealing
training strategy that gradually reduces the thinking reward over time,
allowing the model to rely more on the accurate rule-based outcome reward in
later training stages. Experiments show that our SophiaVL-R1 surpasses a series
of reasoning MLLMs on various benchmarks (e.g., MathVisita, MMMU),
demonstrating strong reasoning and generalization capabilities. Notably, our
SophiaVL-R1-7B even outperforms LLaVA-OneVision-72B on most benchmarks, despite
the latter having 10 times more parameters. All code, models, and datasets are
made publicly available at https://github.com/kxfan2002/SophiaVL-R1.
An enhanced multimodal language model incorporates thinking process rewards to improve reasoning and generalization, achieving superior performance on benchmarks compared to larger models.
multimodal large language models rule-based reinforcement learning reward signals thinking reward model Trust-GRPO method thinking reward comparison annealing training strategy reasoning MLLMs MathVisita MMMU