World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning
Summary
This paper proposes Privileged-Future On-Policy Self-Distillation (PF-OPSD) for controlled concrete reasoning, combining world models' visual simulation with language models' abstract reasoning to improve prediction accuracy and robustness on two new benchmarks.
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Paper page - World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning
Source: https://huggingface.co/papers/2606.03603
Abstract
Controlled concrete reasoning combines visual simulation with abstract reasoning through a training method that uses privileged future information to improve prediction accuracy and robustness.
World modelsandmultimodal large language models(MLLMs) provide complementary capabilities for predicting future outcomes from static visual observations.World modelscan generate concretevisual rolloutsof possible futures, while MLLMs can reason abstractly over questions, goals, and rules. However, generated rollouts are stochastic and may be visually plausible but task-incorrect, making it necessary to determine whenvisual simulationis useful, whether a rollout is credible, and how it should influence the final answer. We formulate this problem ascontrolled concrete reasoning, where a model learns to invoke, verify, and integrate visual future simulation alongsideabstract reasoning. To study this setting, we construct two human-verified benchmarks, VRQABench for controllable spatial lookahead and OpenWorldQA for open-domain physical prediction, and propose Privileged-FutureOn-Policy Self-Distillation(PF-OPSD). During training,PF-OPSDuses ground-truth future videos and answers only as teacher-sideprivileged contextto evaluate on-policy concrete-reasoning trajectories, while the deployable student never observes true futures at test time. Experimental results show thatPF-OPSDoutperforms baseline by 10.6% and 10.9% on VRQABench and OpenWorldQA, respectively, while increasing robustness to noisy or conflicting rollouts. Our code and dataset are available at https://github.com/yczhou001/PF-OPSD.
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