Pith Number

pith:QPK55XOE

pith:2026:QPK55XOEOCON22UGJKKJS3DQKV

not attested not anchored not stored refs pending

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload

Avinash Maurya, Bogdan Nicolae, Franck Cappello, Hui Zhou, Paul Hovland, Ruijie Zhang, Sheng Di, Zhengyang Wang, Zheng Zhang, Ziyue Liu

ReCoVer keeps the per-iteration gradient distribution identical to failure-free LLM pre-training by holding microbatch count constant after any GPU losses.

arxiv:2605.11215 v2 · 2026-05-11 · cs.DC · cs.AI

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\usepackage{pith}
\pithnumber{QPK55XOEOCON22UGJKKJS3DQKV}

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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

ReCoVer successfully preserves the training trajectory from a failure-free reference despite of 256 GPUs lost spread across the run. For comparison with checkpoint-and-restart baselines, ReCoVer demonstrates 2.23× higher effective throughput after successive failures. This advantage results in ReCoVer processing 74.9% more tokens at 234 GPU-hours.

C2weakest assumption

The assumption that maintaining a constant number of microbatches per iteration across survivors, combined with the fault-tolerant collectives and in-step recovery, produces gradients that are stochastically equivalent to a failure-free run without introducing bias or divergence over long training.

C3one line summary

ReCoVer uses fault-tolerant collectives, in-step recovery, and dynamic microbatch redistribution to maintain training trajectory equivalence under GPU failures, delivering 2.23x higher effective throughput than checkpoint-restart on up to 512 GPUs with 256 failures.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-25T02:01:23.073962Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

83d5deddc4709cdd6a864a94996c7055788aa593dd016cfe512bacfeae05da21

Aliases

arxiv: 2605.11215 · arxiv_version: 2605.11215v2 · doi: 10.48550/arxiv.2605.11215 · pith_short_12: QPK55XOEOCON · pith_short_16: QPK55XOEOCON22UG · pith_short_8: QPK55XOE

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/QPK55XOEOCON22UGJKKJS3DQKV \
  | 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: 83d5deddc4709cdd6a864a94996c7055788aa593dd016cfe512bacfeae05da21

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bc2338b3a12770977420da8129c7e8404ca43f5fe48360a1c4d2aa3f6349b47c",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.DC",
    "submitted_at": "2026-05-11T20:28:31Z",
    "title_canon_sha256": "91e4fbfce92febf85a941d4a82beae08b28fc6555cf7a0e0e3d3ff02ac94ab6b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.11215",
    "kind": "arxiv",
    "version": 2
  }
}