ReFoCUS: Reinforcement-guided Frame Optimization for Contextual
  Understanding
Authors: name: Hosu Lee, name: Junho Kim, name: Hyunjun Kim, name: Yong Man Ro

Recent progress in Large Multi-modal Models (LMMs) has enabled effective
vision-language reasoning, yet the ability to understand video content remains
constrained by suboptimal frame selection strategies. Existing approaches often
rely on static heuristics or external retrieval modules to feed frame
information into video-LLMs, which may fail to provide the query-relevant
information. In this work, we introduce ReFoCUS (Reinforcement-guided Frame
Optimization for Contextual UnderStanding), a novel frame-level policy
optimization framework that shifts the optimization target from textual
responses to visual input selection. ReFoCUS learns a frame selection policy
via reinforcement learning, using reward signals derived from a reference LMM
to reflect the model's intrinsic preferences for frames that best support
temporally grounded responses. To efficiently explore the large combinatorial
frame space, we employ an autoregressive, conditional selection architecture
that ensures temporal coherence while reducing complexity. Our approach does
not require explicit supervision at the frame-level and consistently improves
reasoning performance across multiple video QA benchmarks, highlighting the
benefits of aligning frame selection with model-internal utility.