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DART-SD is a topology-aware self-distillation
framework for training

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multi-turn tool-calling agents.

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Instead of forcing an agent to imitate whole
teacher trajectories,

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it identifies the exact point where a student first
leaves the

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recoverable region and supervises only the recovery
steps that follow,

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protecting the valid reasoning the model already
discovered.

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Equipping compact language models with multi-turn
tool use is essential

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for autonomous agents,

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but current distillation relies on imitating full
teacher trajectories.

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For tasks with multiple order-independent sub-goals,

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valid solutions form a vast diamond lattice.

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Forcing this rich topology into a single linear
trajectory collapses it,

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penalizing valid alternative explorations and
severely degrading the

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policy's diversity.

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The prevailing paradigms all mishandle the
structure of exploration.

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SFT teacher boosting overwrites valid exploratory
steps with a global

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loss,

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while standard reinforcement learning misassigns
credit through a

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uniformly distributed reward.

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Traditional hindsight methods still treat
interactions as strict linear

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sequences,

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confusing fatal errors with harmless exploration.

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What is missing is a way to correct only where the
student actually goes

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wrong.

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DART-SD makes three moves.

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First,

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it models the execution process as a converging
Interaction-State

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Transition Graph that faithfully captures the
diamond topology of

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successful and failed paths.

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Second,

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during autonomous rollouts it identifies the
Critical Topological

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Breakpoint and retrieves success-supported recovery
references.

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Third,

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it introduces progressive self-distillation with
breakpoint-localized

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supervision,

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so training loss falls only on generated recovery
steps while the valid

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reasoning prefix is protected.

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DART-SD first abstracts tool responses into
information atoms and builds

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an Interaction-State Transition Graph of main and
auxiliary nodes.

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It defines a task-specific reachability budget and
a success-reachable

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region containing teacher nodes whose remaining
distance to success is

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within that budget.

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Failed student states are projected onto this
region by type,

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and the first transition from a projectable to a
non-projectable state

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is the Critical Topological Breakpoint.

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An augmented generator,

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conditioned on the retained student prefix and
privileged teacher

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references,

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produces a recovery continuation,

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and a masked causal language-modeling loss is
applied only to the

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assistant tokens generated after the breakpoint.

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Models are trained on the FTRL dataset,

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which comprises over two thousand automatically
constructed tool-use

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environments with verifiable feedback across four
task structures:

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single,

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parallel-single, multi, and parallel-multi.

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Evaluation spans five benchmarks: FTRL as the
in-domain test set,

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and BFCL, ToolHop, tau-bench,

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and RoTBench as out-of-domain generalization tests.

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Students are Qwen3-4B and Qwen3-8B,

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distilled from a mixed pool of larger teacher models.

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DART-SD delivers consistent gains on both the
in-domain FTRL test set

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and four out-of-domain benchmarks,

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achieving the best overall average with both the 8B
and 4B backbones.

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Remarkably, the 8B student surpasses its much
larger teacher on FTRL,

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ToolHop, and tau-bench,

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showing that topology-aware recovery paths extract
generalizable

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tool-use behaviors rather than merely imitating
trajectories.

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A component ablation confirms every design choice matters.

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Self-distillation alone lifts Solve-F1 from 23.5 to 38.1.

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Adding breakpoint-localized supervision raises it to 39.5,

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progressive self-distillation to 43.9,

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and replacing LLM-judge breakpoint detection with
the topology-aware

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ISTG projection reaches 45.7.

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The components are complementary and together yield
the strongest

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performance.

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The headline numbers tell the story.

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On Qwen3-8B, FTRL Solve-F1 nearly doubles from 23.5
to 45.7,

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and the overall five-benchmark average reaches 45.6.

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The model also becomes more efficient,

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cutting average tool calls from 4.23 to 3.55

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even shorter than the golden references.

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General capabilities are preserved,

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with the average lifting from 43.9 to 49.9.

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The takeaway is simple: don't force a student to
copy an entire teacher

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trajectory.

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Find the exact point where it first leaves the
recoverable region,

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correct only from there, and protect what it
already got right.

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This topology-aware localized correction produces
compact tool-calling

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agents that are more efficient,

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more general,

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and on several benchmarks better than the teachers
they learned from.
