Play to Generalize: Learning to Reason Through Game Play
Developing generalizable reasoning capabilities in multimodal large language
models (MLLMs) remains challenging. Motivated by cognitive science literature
suggesting that gameplay promotes transferable cognitive skills, we propose a
novel post-training paradigm, Visual Game Learning, or ViGaL, where MLLMs
develop out-of-domain generalization of multimodal reasoning through playing
arcade-like games. Specifically, we show that post-training a 7B-parameter MLLM
via reinforcement learning (RL) on simple arcade-like games, e.g. Snake,
significantly enhances its downstream performance on multimodal math benchmarks
like MathVista, and on multi-discipline questions like MMMU, without seeing any
worked solutions, equations, or diagrams during RL, suggesting the capture of
transferable reasoning skills. Remarkably, our model outperforms specialist
models tuned on multimodal reasoning data in multimodal reasoning benchmarks,
while preserving the base model's performance on general visual benchmarks, a
challenge where specialist models often fall short. Our findings suggest a new
post-training paradigm: synthetic, rule-based games can serve as controllable
and scalable pre-text tasks that unlock generalizable multimodal reasoning
abilities in MLLMs.
6847a7ae3ec10bdd8ab4deed
Yunfei Xie
claimed_verified
2025-06-10T15:20:23.788Z
65f6afa36ea40b9a29dd45ca
/avatars/a80a25fa70a89a3bf3d50ea2866f0df1.svg
Yunfei Xie
user
yunfeixie
6847a7ae3ec10bdd8ab4deee
Yinsong Ma
6847a7ae3ec10bdd8ab4deef
Shiyi Lan
6847a7ae3ec10bdd8ab4def0
Alan Yuille
6847a7ae3ec10bdd8ab4def1
Junfei Xiao
claimed_verified
2025-06-11T08:35:59.384Z
64b5ba6060274cbb296d6288
/avatars/67e0343954dda6e92ed3f6e7976f9f87.svg
Junfei Xiao
user
lambertxiao
6847a7ae3ec10bdd8ab4def2
Chen Wei
Play to Generalize: Learning to Reason Through Game Play
Developing generalizable reasoning capabilities in multimodal large language
models (MLLMs) remains challenging. Motivated by cognitive science literature
suggesting that gameplay promotes transferable cognitive skills, we propose a
novel post-training paradigm, Visual Game Learning, or ViGaL, where MLLMs
develop out-of-domain generalization of multimodal reasoning through playing
arcade-like games. Specifically, we show that post-training a 7B-parameter MLLM
via reinforcement learning (RL) on simple arcade-like games, e.g. Snake,
significantly enhances its downstream performance on multimodal math benchmarks
like MathVista, and on multi-discipline questions like MMMU, without seeing any
worked solutions, equations, or diagrams during RL, suggesting the capture of
transferable reasoning skills. Remarkably, our model outperforms specialist
models tuned on multimodal reasoning data in multimodal reasoning benchmarks,
while preserving the base model's performance on general visual benchmarks, a
challenge where specialist models often fall short. Our findings suggest a new
post-training paradigm: synthetic, rule-based games can serve as controllable
and scalable pre-text tasks that unlock generalizable multimodal reasoning
abilities in MLLMs.
Post-training multimodal large language models with reinforcement learning on arcade-like games enhances their multimodal reasoning abilities without domain-specific data.
multimodal large language models MLLMs Visual Game Learning ViGaL reinforcement learning RL MathVista MMMU multimodal reasoning benchmarks generalizable reasoning skills