pith:7XEA25FQ
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
A unified framework makes self-distillation a reliable way to adapt large language models without stronger teachers.
arxiv:2605.06597 v2 · 2026-05-07 · cs.CL · cs.AI · cs.LG
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Record completeness
Claims
UniSDfull, an integrated pipeline that combines complementary components, achieves the strongest overall performance, improving over the base model by +5.4 points and the strongest baseline by +2.8 points.
That the listed mechanisms (multi-teacher agreement, EMA, token contrastive learning, feature matching, divergence clipping) reliably address supervision instability in free-form self-generated trajectories and that their interactions can be isolated and combined without post-hoc selection bias affecting the reported gains.
UniSD unifies complementary self-distillation mechanisms for autoregressive LLMs and achieves up to +5.4 point gains over base models and +2.8 over baselines across six benchmarks and six models.
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Receipt and verification
| First computed | 2026-05-22T02:04:41.944621Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7XEA25FQG2QRI2FNXXF7MDASVD \
| jq -c '.canonical_record' \
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# expect: fdc80d74b036a11468adbdcbf60c12a8fba5c294aa7459c60fd9f78cb640d93d
Canonical record JSON
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