OPEN-1B: A Fully Auditable Training Run

• URL: http://arxiv.org/abs/2609.17380v1
• Date: Tue, 15 Sep 2026 16:23:08 GMT
• Title: OPEN-1B: A Fully Auditable Training Run
• Abstract summary: We introduce a new tier of model transparency, fully auditable, in which every operation on every data sample during training is independently reproducible on commodity hardware.<n>We make it possible to replay any individual step of a large, distributed training run on a single piece of commodity hardware and check it against the published trajectory.<n>We release Open-1B, a model trained under this regime, together with its full pretraining dataset, every intermediate checkpoint, the training, and the audit harness needed to reproduce and verify any step of its training.
• Score: 0.7223361655030193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-source language models have a reproducibility problem. Despite releasing weights, training data, and recipes, none of them are provably reproducible due to the non-associativity of floating-point arithmetic. Deep learning frameworks often offer a deterministic execution mode, allowing reproducible operations on the same machines. Unfortunately, this determinism does not carry across hardware such that a user can verify that a released checkpoint was actually produced using the declared training recipe. This leaves room for undisclosed data, injected biases, or backdoors that existing techniques such as proof-of-learning or proof-of-training-data cannot rule out. We introduce a new tier of model transparency, fully auditable, in which every operation on every data sample during training is independently reproducible on heterogeneous commodity hardware with bitwise certainty. By imposing a definite order on the sources of training nondeterminism, GPU kernel reductions, data batch ordering across a data-parallel cluster, and inter/intra-node collective communication, we make it possible to replay any individual step of a large, distributed training run on a single piece of commodity hardware and check it against the published trajectory. Because replaying an entire run on one machine is infeasible, we support this with a collective verification scheme in which many independent auditors each certify individual steps, together covering the whole run. We release Open-1B, a model trained under this regime, together with its full pretraining dataset, every intermediate checkpoint, the training codebase, and the audit harness needed to reproduce and verify any step of its training.

Related papers

• AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows [0.0]
  AuditWeave is a lightweight Python library that records the steps of AI-assisted and data-transformation into a single hash-chained ledger.<n>We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation.
  arXiv  Detail & Related papers  (2026-06-14T21:46:04Z)
• Privacy Auditing with Zero (0) Training Run [24.856718399464636]
  We introduce Zero-Run privacy auditing, a post-hoc framework for auditing models using two fixed datasets.<n>Our first approach models the combined effect of distribution shift and algorithmic leakage as an adaptive composition.<n>Our second approach conditions on observed data and adjusts pointwise membership guesses, yielding sharper instance-dependent bounds.
  arXiv  Detail & Related papers  (2026-05-14T09:00:56Z)
• Fine-Grained Traceability for Transparent ML Pipelines [10.182692014427909]
  FG-Trac is a model-agnostic framework that establishes fine-grained sample-level traceability throughout machine learning pipelines.<n>It defines an explicit mechanism for capturing and verifying sample lifecycle events across preprocessing and training.<n>It computes contribution scores explicitly grounded in training checkpoints, and anchors these traces to tamper-evident cryptographic commitments.
  arXiv  Detail & Related papers  (2026-01-21T13:21:30Z)
• Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior [58.751981587234916]
  This paper formalizes the Refinement Provenance Inference (RPI) audit task as Refinement Provenance Inference (RPI)<n>We propose RePro, a logit-based framework that fuses teacher-forced likelihood features with logit-ranking signals.<n>During training, RePro learns a transferable representation via shadow fine-tuning, and uses a lightweight linear head to infer provenance on unseen victims without training-data access.
  arXiv  Detail & Related papers  (2026-01-05T10:16:41Z)
• On the Impossibility of Retrain Equivalence in Machine Unlearning [43.39599739799909]
  Machine unlearning seeks to selectively remove the "influence" of specific training data on a model's outputs.<n>The ideal goal is Retrain Equivalence--behavior identical to a model trained from scratch on only the retained data.<n>Modern pipelines often involve multi-stage training, with each stage having a distinct data distribution and objective.
  arXiv  Detail & Related papers  (2025-10-18T19:58:31Z)
• CountingDINO: A Training-free Pipeline for Class-Agnostic Counting using Unsupervised Backbones [7.717986156838291]
  Class-agnostic counting (CAC) aims to estimate the number of objects in images without being restricted to predefined categories.<n>Current exemplar-based CAC methods rely heavily on labeled data for training.<n>We introduce CountingDINO, the first training-free exemplar-based CAC framework.
  arXiv  Detail & Related papers  (2025-04-23T09:48:08Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent [97.64313409741614]
  We propose to enforce a emphconsistency property which states that predictions of the model on its own generated data are consistent across time. We show that our novel training objective yields state-of-the-art results for conditional and unconditional generation in CIFAR-10 and baseline improvements in AFHQ and FFHQ.
  arXiv  Detail & Related papers  (2023-02-17T18:45:04Z)
• BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning [93.38239238988719]
  We propose to enable deep neural networks with the ability to learn the sample relationships from each mini-batch. BatchFormer is applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications.
  arXiv  Detail & Related papers  (2022-03-03T05:31:33Z)
• Jigsaw Clustering for Unsupervised Visual Representation Learning [68.09280490213399]
  We propose a new jigsaw clustering pretext task in this paper. Our method makes use of information from both intra- and inter-images. It is even comparable to the contrastive learning methods when only half of training batches are used.
  arXiv  Detail & Related papers  (2021-04-01T08:09:26Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.