1
00:00:00,280 --> 00:00:07,820
Which moved first, the blue circle or the yellow square? The
question alone does not specify either event order.

2
00:00:08,760 --> 00:00:14,820
With both frames hidden, an answer could be a guess; a correct
guess would not establish that anything was observed.

3
00:00:15,980 --> 00:00:22,400
Now reveal two original event views: the blue circle moves in
the first, before the yellow square changes position.

4
00:00:23,420 --> 00:00:29,460
The visible sequence supports blue-circle first, linking the
answer to evidence rather than object expectations.

5
00:00:30,680 --> 00:00:36,480
These are neutral geometric events, not footage of real people
or a recreated question from a research benchmark.

6
00:00:37,500 --> 00:00:42,480
When a model answers correctly, did the video supply information
necessary to reach that answer?

7
00:00:43,100 --> 00:00:49,320
Compare that event-order question with asking what color a shape
becomes after it is painted with red paint.

8
00:00:50,200 --> 00:00:56,340
The wording suggests red without video, so success may reflect
text rather than visual understanding.

9
00:00:57,400 --> 00:01:03,140
A video may show the same answer, but its presence does not make
the question depend on visual evidence.

10
00:01:04,260 --> 00:01:09,800
Our first question leaves the order open, because either object
could move first in an otherwise plausible scene.

11
00:01:10,760 --> 00:01:16,760
That separates evidence required for an answer from evidence
merely present while the answer is already inferable.

12
00:01:17,820 --> 00:01:24,160
The paper calls text-based success linguistic shortcutting,
motivating a video-removal test of answerability.

13
00:01:24,860 --> 00:01:30,500
The model receives only the question and options, with video
content and subtitles entirely withheld.

14
00:01:31,480 --> 00:01:37,780
Correct answers define text-only answerable items for that
model; other questions are treated as visually grounded.

15
00:01:38,840 --> 00:01:45,040
These labels describe an operational test, so the distinction
depends on which model performs the text-only assessment.

16
00:01:46,180 --> 00:01:53,620
Shortcuts include wording, world knowledge, implausible-option
elimination, and imagined content that happens to match.

17
00:01:54,660 --> 00:02:00,000
A higher score can include stronger language-only reasoning,
without equal gains in interpreting video.

18
00:02:00,840 --> 00:02:06,620
Testing without visuals reveals this contribution, rather than
crediting every correct answer to visual comprehension.

19
00:02:07,260 --> 00:02:13,200
The paper summarizes roughly forty to sixty percent text-only
answerability on the examined benchmarks.

20
00:02:13,800 --> 00:02:19,460
The exact proportion depends on the benchmark and assessing
model, not a universal set of video questions.

21
00:02:20,440 --> 00:02:28,520
Table one reports GPT-five text-only accuracy of forty-eight
point two on VideoMME, with video input withheld.

22
00:02:29,380 --> 00:02:36,340
For multiple-choice MMVU, the score is fifty-seven point one;
chance accuracy differs across benchmark designs.

23
00:02:37,460 --> 00:02:43,620
These correct-answer rates without video show why accuracy alone
cannot isolate the added value of watching.

24
00:02:44,420 --> 00:02:50,160
Post-training data has similar shortcut questions, which can
reward language priors instead of visual reasoning.

25
00:02:50,800 --> 00:02:57,700
VidGround curates Video-R1 post-training data, containing both
video and image question-answer examples.

26
00:02:58,439 --> 00:03:05,100
GPT-five mini sees questions and options; selection keeps those
it cannot correctly answer without visuals.

27
00:03:05,860 --> 00:03:12,100
The retained pool has one hundred eighty-one thousand seven
hundred ten examples, sixty-nine point one percent.

28
00:03:12,840 --> 00:03:18,680
The full pool has two hundred sixty-three thousand seventy-one
examples, mixing video and image questions.

29
00:03:19,600 --> 00:03:25,500
This training-data ratio differs from the benchmark diagnosis,
which measures answerability on separate datasets.

30
00:03:26,480 --> 00:03:33,120
Text-only failure defines selection; it does not guarantee every
retained question has no possible linguistic shortcut.

31
00:03:33,653 --> 00:03:40,353
The approach combines data selection with RL post-training,
targeting examples that require visual evidence.

32
00:03:41,253 --> 00:03:47,833
Qwen two point five VL seven B Instruct is trained with sixteen
uniformly sampled frames per video.

33
00:03:48,493 --> 00:03:54,893
Training uses seven hundred steps with the GRPO-based
optimization and temporal rewards stated in the source.

34
00:03:55,993 --> 00:04:05,093
VideoMME, VideoMMMU, and multiple-choice MMVU are evaluated with
sixteen, thirty-two, or sixty-four video frames.

35
00:04:06,092 --> 00:04:11,693
Full benchmark and visually grounded subset averages use
separate populations, which must not be mixed.

36
00:04:12,533 --> 00:04:18,173
The ablation compares full-pool and selected-pool training,
isolating the effect of data composition.

37
00:04:18,780 --> 00:04:26,040
At sixty-four frames, unfiltered-pool GRPO reports a fifty-three
point three Full average in Table three.

38
00:04:27,160 --> 00:04:33,320
Training on the selected visually grounded pool reaches
fifty-nine point five on that same full-benchmark average.

39
00:04:34,340 --> 00:04:41,160
The six-point-two-percentage-point difference belongs to this
model, data comparison, frame count, and population.

40
00:04:42,160 --> 00:04:48,380
The visually grounded subset instead compares forty-two point
nine with forty-seven point nine: five points.

41
00:04:49,260 --> 00:04:55,860
Thus the reported up-to-six-point-two improvement is not the
gain for every frame setting, task, or subset.

42
00:04:56,760 --> 00:05:02,860
The result supports checking which examples require visual
evidence, rather than assuming more data must be better.

43
00:05:03,467 --> 00:05:09,807
Event order needs the displayed sequence, while a leading color
question can already suggest its answer.

44
00:05:10,887 --> 00:05:16,187
VidGround screens text-only answerability before selecting
examples to guide post-training.

45
00:05:17,007 --> 00:05:23,107
Selected examples provide visual-dependent supervision under the
filter, alongside an existing RL approach.

46
00:05:24,047 --> 00:05:30,867
Measured gains are configuration-specific; the deeper lesson
concerns the evidence that training examples reward.

47
00:05:31,907 --> 00:05:38,227
Evaluation should separate language-only success from the extra
contribution of watching the actual video.

48
00:05:39,087 --> 00:05:44,647
For a model meant to watch before answering, the training
question should make the visual evidence matter to the answer.