Search papers, labs, and topics across Lattice.
STRIVE, a new structured reinforcement learning framework, improves video question answering by addressing the problem of low reward variance in group-based policy optimization. It constructs multiple spatiotemporal variants of each input video and performs joint normalization across both textual generations and visual variants, enriching reward signals. Experiments on six video reasoning benchmarks demonstrate consistent improvements over strong reinforcement learning baselines across multiple large multimodal models.
Injecting structured visual perturbations into multimodal RL significantly stabilizes training and boosts video reasoning performance, even when answers are superficially similar.
We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for video question answering. While group-based policy optimization methods have shown promise in large multimodal models, they often suffer from low reward variance when responses exhibit similar correctness, leading to weak or unstable advantage estimates. STRIVE addresses this limitation by constructing multiple spatiotemporal variants of each input video and performing joint normalization across both textual generations and visual variants. By expanding group comparisons beyond linguistic diversity to structured visual perturbations, STRIVE enriches reward signals and promotes more stable and informative policy updates. To ensure exploration remains semantically grounded, we introduce an importance-aware sampling mechanism that prioritizes frames most relevant to the input question while preserving temporal coverage. This design encourages robust reasoning across complementary visual perspectives rather than overfitting to a single spatiotemporal configuration. Experiments on six challenging video reasoning benchmarks including VideoMME, TempCompass, VideoMMMU, MMVU, VSI-Bench, and PerceptionTest demonstrate consistent improvements over strong reinforcement learning baselines across multiple large multimodal models. Our results highlight the role of structured spatiotemporal exploration as a principled mechanism for stabilizing multimodal reinforcement learning and improving video reasoning performance.
