VideoKR: 邁向知識與推理密集型影片理解

摘要

我們提出VideoKR，這是首個專門用於強化知識密集型與推理密集型影片理解的大規模訓練語料庫。該語料庫包含31.5萬個影片推理範例，涵蓋14.5萬部新收集、採用CC授權的專家領域影片。我們開發了一套人機協作、以技能為導向的範例生成流程，旨在逐步提升更深層的影片推理能力，同時確保範例及其思維鏈(CoT)推論過程的難度、多樣性與可靠性。我們還整理了新的專家標註基準VideoKR-Eval，其中問題需要真正的影片理解與知識密集型推理，而非依賴文字捷徑。實驗結果顯示，在標準的SFT→GRPO訓練流程下，使用VideoKR進行後訓練的模型在知識密集型影片推理上優於先前的後訓練方法，同時在一般影片推理上仍具競爭力，凸顯資料設計是推動影片推理進展的關鍵因素。我們進一步進行全面的消融實驗，以釐清VideoKR的各項貢獻，為未來研究提供可行的見解。

English

We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFTrightarrowGRPO pipeline, models post-trained on VideoKR outperform prior post-training approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.