Title: FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting
Primary area: foundation or frontier models, including LLMs
Keywords: LVLMs, Video Reasoning, Reinforcement Learning

Abstract: While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame sampling and static textual reasoning, which are inefficient and struggle to handle visually intensive video tasks. 
To overcome these challenges, in this paper, we introduce 
the concept of thinking with long videos and propose a novel framework FrameThinker. Within this framework, LVLMs are able to iteratively interrogate video content. 
Cultivating such video reasoning capabilities in LVLMs presents notable challenges, particularly in adapting the model to new video actions (e.g. select frame), and designing reward functions to guide LVLMs to adopt the newly introduced action. 
To solve these challenges, we introduce a two-phase training approach that begins with Supervised Fine-Tuning (SFT) to instill fundamental action capabilities. 
Subsequently, we leverage Reinforcement Learning (RL) to learn a strategic policy for action decision-making. 
Notably, in this RL phase, we conduct an in-depth and comprehensive exploration of the reward design for each action and format reward. 
Extensive experiments on reasoning benchmarks like Video-Holmes, LongVideo-Reason, and long-video understanding benchmarks such as LongVideoBench, MLVU, VideoMME, and LVBench, demonstrate that FrameThinker get a significant average improvement of +10.4\% over baseline, with a drastic reduction in the number of frames processed. 
Most notably, FrameThinker establishes a new state-of-the-art on LongVideo-Reason, achieving 76.1\% accuracy with a mere 20.6 frames on average. This not only outperforms the competitive LongVILA-R1 (72.0\%) but does so with over 20x fewer frames (vs. 512), demonstrating unparalleled efficiency and effectiveness.

TL;DR: FrameThinker is a framework that equips LVLMs with iterative frame selection for long video reasoning, achieving state-of-the-art accuracy with fewer frames.