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OPEN-1B: A Fully Auditable Training Run

Research LLMs & Foundation Models

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Merged summary

TL;DR - OPEN-1B is a 1B-parameter language model whose distributed training trajectory can be independently replayed and verified bit-for-bit across heterogeneous commodity hardware. This enables auditors to check that published checkpoints genuinely came from the disclosed data, code, and training recipe.

  • The method imposes fixed ordering on GPU reductions, data batches, and inter/intra-node collective communication to eliminate major sources of training nondeterminism.
  • Any individual distributed-training step can be replayed on a single commodity device and compared with the published trajectory.
  • A collective verification scheme distributes individual-step audits across many participants, avoiding the infeasible cost of replaying the entire run on one machine.
  • The release includes the full pretraining dataset, all intermediate checkpoints, training code, and an audit harness.

Sources (1)

OPEN-1B: A Fully Auditable Training Run

arXiv cs.LG John Donaghy, Brian Wilcox, Oğuzhan Ersoy, Shikhar Rastogi, Adam St Arnaud, Alexey Titov, Jordan Greenberg, Ben Fielding, Harry Grieve 2026-09-15 arXiv:2609.17380
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-18 14:16:05.096069 UTC

TL;DR - OPEN-1B is a 1B-parameter language model whose distributed training trajectory can be independently replayed and verified bit-for-bit across heterogeneous commodity hardware. This enables auditors to check that published checkpoints genuinely came from the disclosed data, code, and training recipe.

  • The method imposes fixed ordering on GPU reductions, data batches, and inter/intra-node collective communication to eliminate major sources of training nondeterminism.
  • Any individual distributed-training step can be replayed on a single commodity device and compared with the published trajectory.
  • A collective verification scheme distributes individual-step audits across many participants, avoiding the infeasible cost of replaying the entire run on one machine.
  • The release includes the full pretraining dataset, all intermediate checkpoints, training code, and an audit harness.
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