WEBVTT

1
00:00:00.240 --> 00:00:06.680
Consider an original counting puzzle: three boxes contain four
beads each, then five extra beads are added.

2
00:00:07.260 --> 00:00:13.240
The correct total is seventeen, but an unfinished answer still
contains gaps in its reasoning and final result.

3
00:00:14.020 --> 00:00:19.840
Imagine some words becoming visible while other positions remain
masked, as a response gradually takes shape.

4
00:00:20.620 --> 00:00:25.800
A later, more complete version can contain clues that the
earlier noisy view has not yet made explicit.

5
00:00:26.560 --> 00:00:32.479
These token boxes are an illustrative schematic, not a
reproduced benchmark question or an actual model decoding trace.

6
00:00:33.180 --> 00:00:38.540
Can self-generated later information train a diffusion model to
improve its earlier prediction decisions?

7
00:00:39.167 --> 00:00:46.027
A diffusion language model generates by iterative denoising,
beginning with a response whose token positions are masked.

8
00:00:46.927 --> 00:00:52.387
Each step predicts masked-position distributions, rather than
only the next left-to-right token.

9
00:00:53.187 --> 00:00:59.167
Confident predictions become visible; other positions stay
masked until later steps improve their reliability.

10
00:01:00.007 --> 00:01:06.487
The current noisy state and clean final response are different
moments along one sampled generation trajectory.

11
00:01:07.327 --> 00:01:13.547
Arbitrary-order generation permits conditioning on later
information, rather than only a left-to-right prefix.

12
00:01:14.507 --> 00:01:20.507
During training, a completed self-generated response supplies
privileged teacher-specific context.

13
00:01:21.100 --> 00:01:28.220
The student first samples its own complete trajectory,
preserving earlier noisy states and the final generated
response.

14
00:01:29.200 --> 00:01:35.200
For an earlier denoising step, its input remains that current
noisy state together with the original prompt.

15
00:01:36.340 --> 00:01:42.160
The teacher view selectively reveals tokens from the completed
response at positions that are currently masked.

16
00:01:43.040 --> 00:01:47.900
Revealing a subset supplies clues while leaving prediction work
for the teacher distribution.

17
00:01:48.860 --> 00:01:54.060
Both roles start from the same diffusion model, but different
views inform their next-step predictions.

18
00:01:54.960 --> 00:02:01.060
Future information comes from its sampled response, not time
travel or external knowledge of unseen test answers.

19
00:02:01.673 --> 00:02:06.713
The objective compares student and teacher distributions at the
current denoising step.

20
00:02:07.773 --> 00:02:14.813
Only selected confident positions among the currently masked
tokens actively determine the next state in that step.

21
00:02:15.853 --> 00:02:21.933
The divergence loss is applied to that subset, rather than
treating every position as a left-to-right continuation.

22
00:02:23.013 --> 00:02:29.793
The teacher supplies dense vocabulary-level guidance, giving
more information than the final correctness reward alone.

23
00:02:30.813 --> 00:02:36.533
The default selects supervised masked positions by teacher
confidence, supported by the reported ablation.

24
00:02:37.753 --> 00:02:43.333
Summing this guidance over the sampled trajectory aligns the
objective with the iterative denoising process.

25
00:02:43.953 --> 00:02:50.893
The completed response used for guidance is generated by the
student itself, keeping the trajectory on-policy.

26
00:02:51.752 --> 00:02:57.653
The default recipe samples until a correct final answer appears,
or reaches its budget of eight attempts.

27
00:02:58.613 --> 00:03:06.733
Task-specific checks select useful future information; Sudoku
uses a score threshold rather than binary answer matching.

28
00:03:07.693 --> 00:03:14.293
Training follows the whole sampled trajectory, rather than one
artificial forward pass over a static response.

29
00:03:15.413 --> 00:03:21.193
The default fixes the initial teacher policy, providing a stable
reference as student parameters change.

30
00:03:22.133 --> 00:03:27.073
Teacher information comes from enriched context, while model
updates apply to the learner.

31
00:03:27.547 --> 00:03:34.827
The experiments use LLaDA Instruct, an eight-billion-parameter
diffusion model, on four math and planning tasks.

32
00:03:35.486 --> 00:03:43.087
Tasks include GSM eight K, MATH five hundred, Countdown, and
four-by-four Sudoku, with retained benchmark settings.

33
00:03:43.867 --> 00:03:50.007
Training uses low-rank adaptation and reverse KL, retaining a
quarter of eligible tokens for teacher context.

34
00:03:50.747 --> 00:03:55.547
Training length is two hundred fifty-six tokens; evaluation
lengths depend on the task category.

35
00:03:56.367 --> 00:04:01.627
Evaluation occurs every twenty-five steps before five hundred
one, reporting the best evaluated checkpoint.

36
00:04:02.327 --> 00:04:08.747
The comparison specifies the model, distillation recipe, task,
generation length, and checkpoint-selection rule.

37
00:04:09.193 --> 00:04:17.953
Consider GSM eight K at length two hundred fifty-six, using the
eight-billion-parameter LLaDA Instruct diffusion model.

38
00:04:18.673 --> 00:04:24.113
The base scores seventy-six percent; diffusion GRPO reaches
seventy-nine point eight percent.

39
00:04:24.753 --> 00:04:31.073
Diffusion on-policy self-distillation reaches eighty-one
percent, exceeding that baseline in this specific setting.

40
00:04:32.273 --> 00:04:37.693
Other Table one tasks and generation lengths differ, so the
comparison remains tied to this configuration.

41
00:04:38.653 --> 00:04:44.093
At MATH five hundred length five hundred twelve, diffusion GRPO
reports thirty-nine point two.

42
00:04:44.753 --> 00:04:49.093
The proposed method reports thirty-seven point eight for that
same task and generation length.

43
00:04:49.893 --> 00:04:55.453
The reported improvement therefore does not imply that the
proposed method wins every individual cell in the table.

44
00:04:56.333 --> 00:05:01.833
This chart preserves the precise model, baseline, generation
length, and scope of the reasoning comparison.

45
00:05:02.520 --> 00:05:08.160
Optimization-step counts also describe how quickly dense teacher
guidance can improve the learner.

46
00:05:09.220 --> 00:05:16.620
GSM eight K lists seven thousand seven hundred baseline updates
versus four hundred twenty-five for d-OPSD.

47
00:05:17.500 --> 00:05:23.840
Baseline counts come from its original report: these are
optimization steps, rather than matched wall-clock timings.

48
00:05:24.720 --> 00:05:30.940
Dense supervision is linked to efficiency, while each training
step still spends computation on sampled trajectories.

49
00:05:31.940 --> 00:05:37.860
Return to the bead example: a checked later response guides
earlier masked prediction states.

50
00:05:38.940 --> 00:05:45.560
Ordinary generation evaluates the trained model, whose
parameters improve through training-specific privileged context.

51
00:05:46.640 --> 00:05:53.380
The method pairs self-generated future context with step-aligned
supervision, tailored to iterative diffusion reasoning.