title: ReFoCUS: Reinforcement-guided Frame Optimization for Contextual
    Understanding
summary: 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.
paper_authors._id: 683fe4caf8916dcd6d1c936a
paper_authors.name: Hosu Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:54:26.062Z
paper_authors._id: 673060959e631f353ae1b5e0
paper_authors.avatarUrl: /avatars/d4b1e23de90ff1d02c38186a259b8d1e.svg
paper_authors.fullname: Hosu Lee
paper_authors.type: user
paper_authors.user: lakelee
paper_authors._id: 683fe4caf8916dcd6d1c936b
paper_authors.name: Junho Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:54:30.239Z
paper_authors._id: 653238bed0f5a9e537ed966d
paper_authors.avatarUrl: /avatars/e97a83e68e770baa1c5df847777cf213.svg
paper_authors.fullname: Junho Kim
paper_authors.type: user
paper_authors.user: arkimjh
paper_authors._id: 683fe4caf8916dcd6d1c936c
paper_authors.name: Hyunjun Kim
paper_authors._id: 683fe4caf8916dcd6d1c936d
paper_authors.name: Yong Man Ro
paper_title: ReFoCUS: Reinforcement-guided Frame Optimization for Contextual
    Understanding
paper_summary: 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.
paper_ai_summary: ReFoCUS uses reinforcement learning to optimize frame selection for video-LLM, enhancing reasoning performance in video QA by aligning with model preferences.
paper_ai_keywords: Large Multi-modal Models vision-language reasoning frame selection strategies reinforcement learning frame selection policy reference LMM autoregressive architecture conditional selection architecture temporal coherence video QA benchmarks