VISD: Enhancing Video Reasoning via Structured Self-Distillation

Training VideoLLMs for complex reasoning remains challenging due to sparse sequence-level rewards and the lack of fine-grained credit assignment over long, temporally grounded reasoning trajectories. While reinforcement learning with verifiable rewards (RLVR) provides reliable supervision, it fails to capture token-level contributions, leading to inefficient learning. Conversely, existing self-distillation methods offer dense supervision but lack structure and diagnostic specificity, and often interact unstably with reinforcement learning. In this work, we propose VISD, a structured self-distillation framework that introduces diagnostically meaningful privileged information for video reasoning. VISD employs a video-aware judge model to decompose reasoning quality into multiple dimensions, including answer correctness, logical consistency, and spatio-temporal grounding, and uses this structured feedback to guide a teacher policy for token-level supervision. To stably integrate dense supervision with RL, we introduce a direction–magnitude decoupling mechanism, where environment rewards determine update direction, while structured privileged signals modulate token-level update magnitudes. This design enables semantically aligned and fine-grained credit assignment, improving both reasoning faithfulness and training efficiency. Additionally, VISD incorporates independent advantage estimation, curriculum scheduling, and EMA-based teacher stabilization to support robust optimization over long video sequences. Experiments on various benchmarks demonstrate that VISD consistently outperforms strong baselines across diverse video reasoning tasks, achieving gains in accuracy, grounding quality, and interpretability. Notably, VISD significantly accelerates convergence, highlighting the effectiveness of structured self supervision in improving both performance and sample efficiency for VideoLLMs.