Pith Number

pith:W5RZNYN2

pith:2026:W5RZNYN2LB4FPCQ5CZWCQDGC2M

not attested not anchored not stored refs resolved

Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation

Bing Yin, Changlong Yu, Chengyu Dong, Haoran Liu, Ilgee Hong, Jingbo Shang, Qin Lu, Sha Li, Shuowei Jin, Xintong Li, Yuwei Zhang, Zhenyu Shi

Reflection-Enhanced Self-Distillation lets models learn from failure feedback by creating diagnostic reflections and a reusable global playbook.

arxiv:2605.12741 v1 · 2026-05-12 · cs.LG

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\usepackage{pith}
\pithnumber{W5RZNYN2LB4FPCQ5CZWCQDGC2M}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

RESD substantially outperforms standard self-distillation baselines and achieves significantly faster early-stage improvement than GRPO with 8× samples using only a single rollout per prompt.

C2weakest assumption

That the model-generated retrospective reflections accurately diagnose local errors and that the curated global playbook preserves reusable lessons without introducing noise or compounding errors across training steps.

C3one line summary

RESD turns failure trajectories into token-level supervision via retrospective reflections and a persistent global playbook, enabling faster improvement than standard self-distillation or GRPO with only one rollout per prompt.

References

32 extracted · 32 resolved · 13 Pith anchors

[1] GKD: Generalized knowledge distillation for auto-regressive se- quence models.arXiv preprint arXiv:2306.13649 2023

[2] On-policy distillation of language models: Learning from self-generated mistakes 2024

[3] Retaining by doing: The role of on-policy data in mitigating forgetting, 2025 2025

[4] Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models 2024 · arXiv:2401.01335

[5] Deepseek-v4: Towards highly efficient million-token context intelligence 2026

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T03:09:49.089887Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b76396e1ba5878578a1d166c280cc2d335cf77b5ea15d9f651b4507273e81ef6

Aliases

arxiv: 2605.12741 · arxiv_version: 2605.12741v1 · doi: 10.48550/arxiv.2605.12741 · pith_short_12: W5RZNYN2LB4F · pith_short_16: W5RZNYN2LB4FPCQ5 · pith_short_8: W5RZNYN2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/W5RZNYN2LB4FPCQ5CZWCQDGC2M \
  | 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())"
# expect: b76396e1ba5878578a1d166c280cc2d335cf77b5ea15d9f651b4507273e81ef6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6f65a56df01cf0dd1fd513dcb7360816b6c921d45d91542a9c01ce95339c81d8",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-12T20:46:05Z",
    "title_canon_sha256": "84b5e45e462f5ae833fc31dc3254bca3bc0371a78c94b09cb6a4ed5b273cd3ad"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12741",
    "kind": "arxiv",
    "version": 1
  }
}