Reasoning Beyond Imitation: Observation Conditioned Distillation for Video LLMs

Reasoning distillation offers a promising approach to transfer temporal reasoning capabilities of powerful Video-LLMs to open-source student models. However, teacher and student Video-LLMs may construct different temporal observations from the same video due to the adoption of distinct video sampling pipelines. A reasoning trajectory valid under the teacher's observations may rely on evidence inaccessible to the student and thus cannot be directly executed based on the student's observations. We formalize this as a conditional target mismatch, thereby revealing the Reasoning Acquisition–Execution Gap. To bridge this gap, we propose an acquisition-to-execution framework that first distills teacher trajectories into a student reasoning prior and then further optimizes the execution capability of this prior across multiple observations. We further introduce Execution-Aware Reference Regularization, which retains the existing prior when it is executable under current observations, while allowing a greater degree of adaptive adjustment when it is not. Experiments across multiple Video-LLM backbones and six video reasoning benchmarks show that explicitly optimizing observation-conditioned execution consistently outperforms supervised distillation and standard RL-based optimization, with gains extending to held-out out-of-domain benchmarks.