Researchers propose PhysEvolve, a verifier-grounded self-evolution method for improving physical reasoning in vision-language models, which the authors say remain unreliable on questions about dynamics and physical causality. The approach targets the scarcity of grounded counterfactual training data by pairing model self-evolution with executable worlds that verify reasoning. The work is framed as addressing a key bottleneck in how embodied systems understand and act in the physical world.
Physical reasoning underpins how embodied systems understand and act in the physical world, yet vision-language models (VLMs) remain unreliable on questions about dynamics and physical causality. Progress is critically constrained by the scarcity of grounded counterfactual training data, as observat
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