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#Data and training

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

10/16Fri
  1. Hugging Face Blog82

    NVIDIA and AI4Bharat Introduce Nemotron-pretraining-Indic-v1 and an Open Data Generation Pipeline

    NVIDIA and AI4Bharat at IIT Madras introduce Nemotron-pretraining-Indic-v1, approximately 2.8 trillion tokens across 22 Indian languages, releasing soon alongside its open, GPU-accelerated generation pipeline. The corpus includes native-script text for all 22 languages and romanized text for twelve, translated from curated English sources with quality checks and source tracing. An Indic continued-pretraining stage improved five reported benchmarks, but combined synthetic and natural data, so the comparison does not isolate this corpus’s contribution.

    Why it matters: The workflow offers a reusable approach to multilingual data generation by separating translation, quality assessment, repair, and script conversion while preserving links to source documents.

10/13Tue
  1. Microsoft Research65

    Simulating student learning for better AI tutoring

    Microsoft Research introduces StudentSim, a research system that trains personalized student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors. StudentSimEval measures behavioral fidelity and guidance responsiveness across chess, second-language English writing, and mathematics, where StudentSim outperforms leading baselines. In a chess tutor study, training with StudentSim feedback achieves 90.5% accuracy versus 75.7% without reinforcement learning and 71.6% with GPT-5.4 feedback; real learner studies remain essential, and the paper and code are publicly available.

    Why it matters: Evaluating both a simulated learner’s starting ability and response to guidance offers a concrete method for assessing tutor feedback beyond the correctness of an explanation.

10/9Fri
  1. Hugging Face Blog34

    How Ai2 Rebuilt Cluster Scheduling with GPU Time Budgets

    Ai2 replaced its original priority scheduler with GPU time budgets, hierarchical fair allocation, and time-slice contracts, turning debates over how much GPU time each research project should get from case-by-case operational decisions into a transparent budget allocation process. Its cluster manages thousands of NVIDIA H100, B200, and B300 GPUs, with scales ranging from 88 to 1,024 GPUs, and demand from roughly 150 internal researchers has long been 2-3 times the available compute.

10/8Thu
  1. Microsoft Research72

    Microsoft Research open-sources Agent Lightning v1.0, a 3,500-line lightweight agent RL framework for training with real harnesses

    Microsoft Research open-sourced Agent Lightning v1.0, a lightweight agent reinforcement-learning framework of about 3,500 lines. Its Harnessed Agentic RL approach lets the same agent harness used in deployment participate directly in reinforcement learning, without reimplementing the agent inside a training framework.

    Why it matters: The source gives the framework's code size, Kubernetes support, and training recipe, helping readers assess the cost of bringing a real harness into RL.

10/7Wed
  1. Hugging Face Blog62

    Nemotron fine-tuning reaches gold-medal level at both IOI 2026 and IMO 2026

    The NVIDIA team started with Nemotron 3 and used supervised fine-tuning, reinforcement learning, and a generate-verify-improve reasoning process to build specialized models that reached gold-medal thresholds at both IOI 2026 and IMO 2026.

    Why it matters: The source gives a complete recipe for fine-tuning Nemotron 3 to gold-medal-level results at IOI and IMO, which readers can use to reproduce its data and reasoning workflow.

  2. AWS Machine Learning Blog22

    Management and governance best practices for Amazon SageMaker HyperPod

    Amazon SageMaker HyperPod can connect clusters to projects through SageMaker Unified Studio, letting team members launch training and fine-tuning workloads in workspaces while retaining governance controls at the infrastructure level. The article proposes a four-layer control model—organization, project, cluster, and workload—and explains how to design identity, capacity, and observability policies across those layers. Infrastructure teams retain control of cluster operations while ML teams use approved compute in project contexts.

10/6Tue
  3. Hugging Face Blog26

    Falcon-Emirati-7B: teaching LLMs the Emirati dialect, culture, and feel for language

    Falcon-Emirati-7B is a specialized model for Emirati dialects built on Falcon-H1-Arabic. It aims to understand and generate Emirati Arabic like a native speaker. It uses the Falcon-H1 hybrid architecture, running Mamba alongside Transformer attention. The base model family comes in 3B, 7B, and 34B sizes, with a context window of up to 256K tokens.

10/3Sat
  1. Hugging Face Blog62

    Ai2 open-sources AstaBrief 8B, a scientific report-generation model

    Ai2 open-sourced AstaBrief 8B, a model that turns research questions and retrieved literature excerpts into reports with citations. It was trained from Qwen3-8B using SFT and DPO and is now available as Asta's Fast mode.

    Why it matters: The source describes AstaBrief's training-data construction and citation-filtering details, which can be adapted to deploy other long-form generation models.

10/2Fri
  1. Hugging Face Blog42

    ServiceNow CoreAI launches AutoSynthData to generate training data for enterprise agents

    ServiceNow CoreAI built AutoSynthData to find capability gaps from a target model's failures and a stronger teacher's successful trajectories, then generate and validate new tasks for post-training. As the model improves, the curriculum shifts to tasks that remain difficult. The process was demonstrated on the EnterpriseOps Gym dataset. Generated tasks must meet feasibility, realism, and difficulty requirements; validators must provide consistency, reliability, and completeness.

10/1Thu
  1. Hugging Face Blog62

    AI2 releases Olmo-core 3, open and scalable training infrastructure for large MoE models

    AI2 released Olmo-core 3, a major upgrade to its large language model development framework. It redesigns the open MoE training system to scale MoE training to trillions of parameters while maintaining compute efficiency.

    Why it matters: The source provides throughput and memory comparisons for the MoE training stack, helping readers assess changes in the cost of large-scale MoE training.

  3. NVIDIA Blog22

    How NVIDIA AI factories maximize ROI through high output, long life, and broad applicability

    NVIDIA says AI factory return on investment depends on three factors: output capacity, useful life, and demand. Its platform uses full-stack co-design to maximize throughput per megawatt. SemiAnalysis AgentX data show Vera Rubin NVL72 delivers more than 30 times the throughput per megawatt of GB300 NVL72 and, on DeepSeek V4 Pro, can reduce cost per million tokens by up to 45 times.

9/30Wed
  1. Google DeepMind62

    Google DeepMind launches SynthID Bio to embed watermarks in AI-generated proteins

    Google DeepMind launched SynthID Bio, bringing watermarking to synthetic biology. It embeds imperceptible signatures directly into biological code so that generated content can be verified both in digital models and in synthesized physical proteins, while preserving their biological function.

    Why it matters: SynthID watermarking has expanded from images to synthetic biology, showing how AI-generated proteins can be marked and verified.

  2. NVIDIA Blog62

    NVIDIA and CoreWeave put Vera Rubin NVL72 into production; Cognition is first customer

    CoreWeave announced that NVIDIA Vera Rubin NVL72 systems and Spectrum-X 102.4T Ethernet are available on CoreWeave Cloud, making it one of the first cloud providers to deliver the platform. Cognition is the first customer to run Vera Rubin in production.

    Why it matters: CoreWeave launched Vera Rubin NVL72 and published measured Devin throughput, offering a view of compute costs for agentic coding workloads.

9/29Tue
  1. Hugging Face Blog62

    NVIDIA releases Kumo Tabular, a foundation model for tabular prediction

    NVIDIA has released Kumo Tabular, an open-source tabular foundation model that predicts labels for new rows in a single forward pass from labeled table rows, supporting classification and regression without training, tuning, or feature engineering. The model comes in three sizes from 28M to 215M parameters, was pretrained entirely on synthetic tables, is commercially usable under the OpenMDW-1.1 license, and ranks first on four benchmarks: TabArena, BeyondArena, TALENT, and ScoringBench.

    Why it matters: The source reports model sizes, training-data provenance, and rankings on four benchmarks, helping readers assess the accuracy and efficiency tradeoffs of tabular foundation models.

  2. Microsoft Research62

    Microsoft Research releases Quine, an AI research system for biological complexity

    Microsoft Research has released Quine, an AI research system designed for biological complexity. It combines a biological world model with an interactive harness connecting models, scientific tools, literature, and experimentalists. In work with the Broad Institute on pancreatic ductal adenocarcinoma, it predicted and prioritized thousands of compounds; the top-ranked compound produced the largest classical-to-ground-state transition in wet-lab experiments. The process from narrowing the search space to selecting a candidate for validation took one weekend.

    Why it matters: Microsoft Research describes Quine's multimodal biological world model and experimental loop, including wet-lab validation in pancreatic cancer compound screening.

9/26Sat
  1. AWS Machine Learning Blog36

    Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP boosts throughput by 40%

    AWS presents an architecture for scaling MoE reinforcement-learning training on Amazon EKS with EFA and DeepEP, improving throughput by 40%. It addresses resource contention between rollout generation and policy training in large-scale RL workloads such as RLHF and GRPO, high-throughput communication across hundreds of accelerators, and the cross-node all-to-all overhead introduced by Expert Parallelism.

  3. AWS Machine Learning Blog34

    NarrateAI delivers production-grade LLM quality assurance on Amazon Bedrock

    NarrateAI uses five techniques on Amazon Bedrock to achieve about 99% numerical accuracy while supporting real-time streaming responses: adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, a composite evaluation framework, and data-accuracy validation. Each addresses failure modes such as hallucinated metrics, API throttling, validation delays, and subjective language. Built on Amazon Bedrock AgentCore, the solution serves more than 4,000 AWS executives.

9/25Fri
9/24Thu
  1. NVIDIA Blog60

    NVIDIA, Google DeepMind, and partners open predicted protein-complex structures for more than 2,800 viruses

    NVIDIA, Google DeepMind, EMBL-EBI, and other organizations have made predicted 3D structures of protein complexes from more than 2,800 viruses freely available to any scientist through the AlphaFold Database.

    Why it matters: NVIDIA, Google DeepMind, and collaborators have opened predicted protein-complex structures for more than 2,800 viruses, showing a concrete approach to open science for pandemic preparedness.

9/23Wed
  1. NVIDIA Blog12

    How NVIDIA validation engineer Sakeena Fiza helps hardware succeed at scale

    NVIDIA validation engineer Sakeena Fiza works in the data-center systems engineering lab, validating hardware before new products enter mass production. One of her earliest and most memorable moments was seeing the NVIDIA Rubin GPU enumerate successfully at the system level for the first time. Validation covers the full path from tray, rack, and cluster to production line and customer AI factory, aiming to find problems before customers do.

9/21Mon
  1. Microsoft Research40

    Microsoft open-sources RetroChimera retrosynthesis model in a paper published in Nature

    Microsoft has published RetroChimera, a retrosynthesis-prediction framework, in Nature and open-sourced its implementation and weights. It combines the Transformer model R-SMILES 2 with the GNN model NeuralLoc, using a learned ensemble to rerank their predictions and performing strongly on both common and rare reaction types. In blind tests, chemists preferred its single-step reaction predictions. It found complete routes for nine of ten challenging targets, compared with five for a de novo model, four for an editing model, and two for NeuralSym.

9/19Sat
9/16Wed
  1. NVIDIA Blog62

    NVIDIA Vera Rubin NVL72 makes its MLPerf Inference v6.1 debut with leading performance

    NVIDIA submitted preview results for Vera Rubin NVL72 for the first time in MLPerf Inference v6.1, reporting up to 3.7× the throughput of GB300 NVL72 on Qwen3-VL and up to 2.5× on DeepSeek-R1.

    Why it matters: MLPerf Inference v6.1 reports throughput, scaling efficiency, and software-optimization gains for Vera Rubin NVL72 versus GB300 NVL72, offering a basis for judging inference infrastructure value.

  3. NVIDIA Blog62

    Jensen Huang at Dreamforce: Salesforce's first CRM reasoning model, Koa, is built on NVIDIA Nemotron 3 Super

    At Salesforce Dreamforce, NVIDIA founder and CEO Jensen Huang appeared alongside Marc Benioff as Salesforce released Koa, its first CRM reasoning model, built on NVIDIA Nemotron 3 Super.

    Why it matters: Huang discussed AI safety and shifts in the share of open models at Dreamforce, while the article explains how Salesforce's first reasoning model was trained and when it will be deployed.

  4. Google Research36

    Google Research proposes Retrieve-for-Train: compile diffusion models with RL to bypass inference bottlenecks in complex AI search

    Google Research proposes Retrieve-for-Train, a framework that uses offline RL to discover reward-aligned query fan-outs and compile them into supervision, then distills them into a 53.9M-parameter diffusion retriever. At inference, it generates a complete result set in one non-autoregressive pass without chain-of-thought tokens. The method fine-tunes fan-out language models based on Gemma3-4B and Qwen3-4B, using set-level attribute rewards to address paraphrase collapse in zero-shot LLMs and autoregressive latency.

  5. NVIDIA Blog62

    NVIDIA details DSX platform: MaxLPS delivers 24% more token throughput on the same power budget

    At the AI Infra Summit, NVIDIA presented its DSX platform. DSX MaxLPS was validated on Lambda's five-rack, 19-node HGX B200 cluster, where it increased token throughput by 24% under the same power budget—from about 4 million to 5 million tokens per second—and improved performance per watt by 23%.

    Why it matters: The article reports DSX MaxLPS measurements on a Lambda cluster, allowing readers to judge possible throughput gains under a fixed power budget.

  6. NVIDIA Blog72

    NVIDIA shares efficiency progress for Vera Rubin and DSX at AI Infra Summit

    At the AI Infra Summit, NVIDIA announced several advances in Vera Rubin and its DSX platform, shifting AI infrastructure measurement from peak performance to useful agentic tokens per megawatt.

    Why it matters: The article reports several measured token-output figures per megawatt for Vera Rubin and DSX, showing how efficiency metrics for AI factories are changing.

9/11Fri
9/10Thu