PhysEvolve: Verifier-grounded Self-evolution for Physical Reasoning with Executable Worlds

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
observational videos capture only the factual trajectory that occurred. The
self-evolving paradigm offers a route beyond fixed datasets by enabling models
to construct their own training problems. Extending this paradigm to physics,
however, requires an external source of truth that can both validate
model-proposed environments and verify alternative physical outcomes under
intervention. We introduce \physevolve{}, a verifier-grounded self-evolving
framework that trains VLMs for causal physical reasoning from model-proposed
executable worlds and simulation-authored tasks. At each round, a shared VLM
proposes executable world programs and evidence schemas; a deterministic
physics engine freezes each valid world, simulates matched baseline and
body-elided counterfactual branches, and synthesizes discrete, answer-bound
visual tasks. Exact verifier feedback from task solving updates both the
shared policy via GRPO and an adaptive curriculum over physical intents.
Starting from Qwen3-VL-8B-Instruct, \physevolve{} achieves targeted gains across
PhysBench, CausalPhys, and ContPhy without human intervention data, improving
PhysBench Throwing by $8.8$ points and ContPhy Counterfactual Removal and
Goal-Pit by $7.4$ and $24.4$ points. These gains concentrate on
interaction-sensitive and altered-world questions, demonstrating zero-shot
transfer of causal principles to unseen complex dynamics (cloth, fluid, soft
bodies) and real-world video.