1
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<font color="#FFFFFF">DART-SD is a topology-aware self-distillation
framework for training</font>

2
00:00:05,550 --> 00:00:07,350
<font color="#FFFFFF">multi-turn tool-calling agents.</font>

3
00:00:07,762 --> 00:00:12,422
<font color="#FFFFFF">Instead of forcing an agent to imitate whole
teacher trajectories,</font>

4
00:00:12,710 --> 00:00:17,034
<font color="#FFFFFF">it identifies the exact point where a student first
leaves the</font>

5
00:00:17,046 --> 00:00:22,228
<font color="#FFFFFF">recoverable region and supervises only the recovery
steps that follow,</font>

6
00:00:22,491 --> 00:00:25,540
<font color="#FFFFFF">protecting the valid reasoning the model already
discovered.</font>

7
00:00:26,596 --> 00:00:30,834
<font color="#FFFFFF">Equipping compact language models with multi-turn
tool use is essential</font>

8
00:00:30,846 --> 00:00:32,058
<font color="#FFFFFF">for autonomous agents,</font>

9
00:00:32,321 --> 00:00:37,568
<font color="#FFFFFF">but current distillation relies on imitating full
teacher trajectories.</font>

10
00:00:38,006 --> 00:00:40,793
<font color="#FFFFFF">For tasks with multiple order-independent sub-goals,</font>

11
00:00:41,716 --> 00:00:44,341
<font color="#FFFFFF">valid solutions form a vast diamond lattice.</font>

12
00:00:44,828 --> 00:00:50,302
<font color="#FFFFFF">Forcing this rich topology into a single linear
trajectory collapses it,</font>

13
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<font color="#FFFFFF">penalizing valid alternative explorations and
severely degrading the</font>

14
00:00:54,902 --> 00:00:56,064
<font color="#FFFFFF">policy's diversity.</font>

15
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<font color="#FFFFFF">The prevailing paradigms all mishandle the
structure of exploration.</font>

16
00:01:01,273 --> 00:01:06,760
<font color="#FFFFFF">SFT teacher boosting overwrites valid exploratory
steps with a global</font>

17
00:01:06,773 --> 00:01:07,223
<font color="#FFFFFF">loss,</font>

18
00:01:07,473 --> 00:01:10,748
<font color="#FFFFFF">while standard reinforcement learning misassigns
credit through a</font>

19
00:01:10,760 --> 00:01:12,635
<font color="#FFFFFF">uniformly distributed reward.</font>

20
00:01:13,047 --> 00:01:17,987
<font color="#FFFFFF">Traditional hindsight methods still treat
interactions as strict linear</font>

21
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<font color="#FFFFFF">sequences,</font>

22
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<font color="#FFFFFF">confusing fatal errors with harmless exploration.</font>

23
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<font color="#FFFFFF">What is missing is a way to correct only where the
student actually goes</font>

24
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<font color="#FFFFFF">wrong.</font>

25
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<font color="#FFFFFF">DART-SD makes three moves.</font>

26
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<font color="#FFFFFF">First,</font>

27
00:01:30,594 --> 00:01:34,431
<font color="#FFFFFF">it models the execution process as a converging
Interaction-State</font>

28
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<font color="#FFFFFF">Transition Graph that faithfully captures the
diamond topology of</font>

29
00:01:39,169 --> 00:01:41,031
<font color="#FFFFFF">successful and failed paths.</font>

30
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<font color="#FFFFFF">Second,</font>

31
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<font color="#FFFFFF">during autonomous rollouts it identifies the
Critical Topological</font>

32
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<font color="#FFFFFF">Breakpoint and retrieves success-supported recovery
references.</font>

33
00:01:51,378 --> 00:01:51,853
<font color="#FFFFFF">Third,</font>

34
00:01:52,166 --> 00:01:56,078
<font color="#FFFFFF">it introduces progressive self-distillation with
breakpoint-localized</font>

35
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<font color="#FFFFFF">supervision,</font>

36
00:01:57,028 --> 00:02:02,221
<font color="#FFFFFF">so training loss falls only on generated recovery
steps while the valid</font>

37
00:02:02,233 --> 00:02:03,958
<font color="#FFFFFF">reasoning prefix is protected.</font>

38
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<font color="#FFFFFF">DART-SD first abstracts tool responses into
information atoms and builds</font>

39
00:02:09,985 --> 00:02:14,871
<font color="#FFFFFF">an Interaction-State Transition Graph of main and
auxiliary nodes.</font>

40
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<font color="#FFFFFF">It defines a task-specific reachability budget and
a success-reachable</font>

41
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<font color="#FFFFFF">region containing teacher nodes whose remaining
distance to success is</font>

42
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<font color="#FFFFFF">within that budget.</font>

43
00:02:26,276 --> 00:02:29,350
<font color="#FFFFFF">Failed student states are projected onto this
region by type,</font>

44
00:02:29,663 --> 00:02:34,407
<font color="#FFFFFF">and the first transition from a projectable to a
non-projectable state</font>

45
00:02:34,644 --> 00:02:37,454
<font color="#FFFFFF">is the Critical Topological Breakpoint.</font>

46
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<font color="#FFFFFF">An augmented generator,</font>

47
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<font color="#FFFFFF">conditioned on the retained student prefix and
privileged teacher</font>

48
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<font color="#FFFFFF">references,</font>

49
00:02:44,593 --> 00:02:46,568
<font color="#FFFFFF">produces a recovery continuation,</font>

50
00:02:46,880 --> 00:02:51,208
<font color="#FFFFFF">and a masked causal language-modeling loss is
applied only to the</font>

51
00:02:51,221 --> 00:02:53,833
<font color="#FFFFFF">assistant tokens generated after the breakpoint.</font>

52
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<font color="#FFFFFF">Models are trained on the FTRL dataset,</font>

53
00:02:57,644 --> 00:03:01,644
<font color="#FFFFFF">which comprises over two thousand automatically
constructed tool-use</font>

54
00:03:01,656 --> 00:03:06,563
<font color="#FFFFFF">environments with verifiable feedback across four
task structures:</font>

55
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<font color="#FFFFFF">single,</font>

56
00:03:07,539 --> 00:03:10,326
<font color="#FFFFFF">parallel-single, multi, and parallel-multi.</font>

57
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<font color="#FFFFFF">Evaluation spans five benchmarks: FTRL as the
in-domain test set,</font>

58
00:03:16,630 --> 00:03:19,268
<font color="#FFFFFF">and BFCL, ToolHop, tau-bench,</font>

59
00:03:19,505 --> 00:03:23,686
<font color="#FFFFFF">and RoTBench as out-of-domain generalization tests.</font>

60
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<font color="#FFFFFF">Students are Qwen3-4B and Qwen3-8B,</font>

61
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<font color="#FFFFFF">distilled from a mixed pool of larger teacher models.</font>

62
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<font color="#FFFFFF">DART-SD delivers consistent gains on both the
in-domain FTRL test set</font>

63
00:03:38,348 --> 00:03:41,044
<font color="#FFFFFF">and four out-of-domain benchmarks,</font>

64
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<font color="#FFFFFF">achieving the best overall average with both the 8B
and 4B backbones.</font>

65
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<font color="#FFFFFF">Remarkably, the 8B student surpasses its much
larger teacher on FTRL,</font>

66
00:03:53,746 --> 00:03:55,308
<font color="#FFFFFF">ToolHop, and tau-bench,</font>

67
00:03:56,248 --> 00:04:00,323
<font color="#FFFFFF">showing that topology-aware recovery paths extract
generalizable</font>

68
00:04:01,240 --> 00:04:04,778
<font color="#FFFFFF">tool-use behaviors rather than merely imitating
trajectories.</font>

69
00:04:05,836 --> 00:04:09,174
<font color="#FFFFFF">A component ablation confirms every design choice matters.</font>

70
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<font color="#FFFFFF">Self-distillation alone lifts Solve-F1 from 23.5 to 38.1.</font>

71
00:04:16,123 --> 00:04:21,306
<font color="#FFFFFF">Adding breakpoint-localized supervision raises it to 39.5,</font>

72
00:04:21,594 --> 00:04:24,681
<font color="#FFFFFF">progressive self-distillation to 43.9,</font>

73
00:04:25,043 --> 00:04:29,841
<font color="#FFFFFF">and replacing LLM-judge breakpoint detection with
the topology-aware</font>

74
00:04:29,854 --> 00:04:33,016
<font color="#FFFFFF">ISTG projection reaches 45.7.</font>

75
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<font color="#FFFFFF">The components are complementary and together yield
the strongest</font>

76
00:04:36,866 --> 00:04:37,579
<font color="#FFFFFF">performance.</font>

77
00:04:38,680 --> 00:04:40,193
<font color="#FFFFFF">The headline numbers tell the story.</font>

78
00:04:40,605 --> 00:04:49,419
<font color="#FFFFFF">On Qwen3-8B, FTRL Solve-F1 nearly doubles from 23.5
to 45.7,</font>

79
00:04:49,819 --> 00:04:54,374
<font color="#FFFFFF">and the overall five-benchmark average reaches 45.6.</font>

80
00:04:54,798 --> 00:04:56,636
<font color="#FFFFFF">The model also becomes more efficient,</font>

81
00:04:57,568 --> 00:05:01,518
<font color="#FFFFFF">cutting average tool calls from 4.23 to 3.55</font>

82
00:05:02,440 --> 00:05:04,440
<font color="#FFFFFF">even shorter than the golden references.</font>

83
00:05:04,853 --> 00:05:07,730
<font color="#FFFFFF">General capabilities are preserved,</font>

84
00:05:07,992 --> 00:05:12,192
<font color="#FFFFFF">with the average lifting from 43.9 to 49.9.</font>

85
00:05:13,157 --> 00:05:17,232
<font color="#FFFFFF">The takeaway is simple: don't force a student to
copy an entire teacher</font>

86
00:05:17,245 --> 00:05:17,895
<font color="#FFFFFF">trajectory.</font>

87
00:05:18,357 --> 00:05:22,670
<font color="#FFFFFF">Find the exact point where it first leaves the
recoverable region,</font>

88
00:05:22,920 --> 00:05:26,220
<font color="#FFFFFF">correct only from there, and protect what it
already got right.</font>

89
00:05:26,633 --> 00:05:31,587
<font color="#FFFFFF">This topology-aware localized correction produces
compact tool-calling</font>

90
00:05:31,600 --> 00:05:33,074
<font color="#FFFFFF">agents that are more efficient,</font>

91
00:05:33,399 --> 00:05:34,037
<font color="#FFFFFF">more general,</font>

92
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<font color="#FFFFFF">and on several benchmarks better than the teachers
they learned from.</font>
