VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

Existing Multimodal Large Language Models (MLLMs) of-ten suffer from hallucinations in long video understanding (LVU), pri-marily due to the imbalance between textual and visual tokens. Ob-serving that MLLMs handle shorter but more accurate visual inputswell, recent LVU works alleviate hallucinations by automatically parsingthe vast visual data into manageable segments that can be effectivelyprocessed by MLLMs. SFT-based tool-calling methods can serve thispurpose, but they typically require vast amounts of fine-grained, high-quality data and suffer from constrained tool-calling trajectories. Wepropose a novel VideoTIR that leverages Reinforcement Learning (RL)to encourage proper usage of comprehensive multi-level toolkits for ef-ficient long video understanding. VideoTIR explores both Zero-RL andSFT cold-starting to enable MLLMs to retrieve and focus on meaning-ful video segments/images/regions, enhancing long video understandingboth accurately and efficiently. To reduce redundant tool-calling in theearly RL-stage and accelerate convergence, we propose Toolkit ActionGrouped Policy Optimization (TAGPO), which enhances the efficiencyof the calling process through the finer stepwise reward assignment. Ad-ditionally, we develop a sandbox-based trajectory synthesis frameworkto generate high-quality trajectory data. Extensive experiments on threelong-video QA benchmarks demonstrate the effectiveness and efficiencyof our method.