ChapterNav: Learning Video Chaptering through Verifiable Navigation Feedback

Automatic video chaptering organizes long instructional videos into reusable directories for efficient content access. Current models typically learn from annotated directories through supervised fine-tuning (SFT) or reference-based optimization. However, a reference directory represents only one plausible organization of a video, and optimizing agreement with it can favor annotation-specific predictions over alternative but equally valid chapter structures. More importantly, higher reference agreement does not necessarily imply greater downstream utility: a directory closer to the ground truth may not be more effective for helping users locate the content they need. We introduce ChapterNav, a framework for evaluating and optimizing video chaptering through verifiable navigation utility. Since chaptering admits multiple valid outputs and lacks a directly verifiable target sequence, ChapterNav instead measures whether a generated directory enables a fixed navigator to locate content satisfying a given information need. Given a query and chapter titles, the navigator selects chapters, while a verifier checks whether the retrieved intervals contain the target content within a prescribed viewing budget. We build navigation benchmarks on two datasets and introduce Successful Navigation Gain (SNG) to quantify navigation utility. The resulting feedback, combined with temporal alignment rewards, is further used for reinforcement learning with verifiable rewards (RLVR). In a paired human study on 50 videos, SNG agrees with human navigation preferences on 76% of pairs, compared with 68% for reference-based evaluation. RLVR achieves a relative improvement of 3.8% on the SNG metric compared with SFT, and a relative improvement of 5.2% compared with reference-reward. These results demonstrate the effectiveness of evaluating and learning video chaptering through downstream content-access utility.