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pith:7XEA25FQ

pith:2026:7XEA25FQG2QRI2FNXXF7MDASVD
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UniSD: Towards a Unified Self-Distillation Framework for Large Language Models

B. Aditya Prakash, Haoxin Liu, Jindong Wang, Josiah Hester, Lucheng Fu, Srijan Kumar, Yijia Xiao, Yinyi Luo, Yiqiao Jin, Yiyang Wang

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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Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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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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

fdc80d74b036a11468adbdcbf60c12a8fba5c294aa7459c60fd9f78cb640d93d

Aliases

arxiv: 2605.06597 · arxiv_version: 2605.06597v2 · doi: 10.48550/arxiv.2605.06597 · pith_short_12: 7XEA25FQG2QR · pith_short_16: 7XEA25FQG2QRI2FN · pith_short_8: 7XEA25FQ
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/7XEA25FQG2QRI2FNXXF7MDASVD \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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