293 summarised stories about Model Training, each linking back to the original source. Browse all topics →
Ars Technica 1 month ago 27
A lawsuit accuses xAI of training Grok models using child sex abuse material. The complaint says the Canadian Centre for Child Protection identified AI-generated CSAM on xAI that depicted the plaintiff. As a result, regulators and courts are likely to intensify scrutiny of Grok’s training data and xAI may face further legal action.
Google Research 1 month ago 5
Google introduced the Planetary Prediction Engine (PPE) within Google Earth AI, an experimental system that autonomously runs the full geospatial modeling workflow from natural-language queries. It reports improving US CDC public-health indicator prediction to a mean R² of 76.8% versus 60.0% for a manual expert pipeline. As a result, model building time is reduced from weeks with manual data engineering to minutes, and the system can generate benchmark improvements without manual intervention across multiple prediction tasks.
Tech.eu 1 month ago 32
ColibriTD raised seed funding to scale its multiphysics quantum simulation platform built around the H-DES variational solver.
MarkTechPost 1 month ago 51 ● 2 sources
Google Research and UNSW Sydney introduced GlucoFM, a self-supervised foundation model that splits continuous glucose monitoring traces into separate slow state and transient event streams. It has 0.72M trainable parameters and reached 58.8 task-averaged PR-AUC across 14 cohort–task evaluations. The work is a research prototype with no regulatory clearance and no public checkpoint as of 26 August 2026, but the code/reproducibility plan and CPU/24-hour inference recipe change deployment from model use to a reproducible training workflow.
Apple Machine Learning Research 1 month ago 46
The authors propose a rubric-based reward framework that generates query-specific, evidence-grounded rubrics and uses them for post-training to supervise open-domain question answering. Averaged across three evaluation axes (composition, grounding, and instruction-following), it improves 6.5% over an instruction-tuned baseline. The approach changes training by replacing a single scalar reward with multi-dimensional rubrics conditioned on retrieved evidence, yielding more factual support and better coherence and instruction adherence.
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TLDRocket
24 October 2026
AI’s dominant theme today is accountability at scale: as agents get more autonomous, companies are doubling down on verification, sandboxing, and evidence—because “it worked once” is no longer good enough. Anthropic reports its merged code changes jumped from about 2,500 per day in May to over 30,000 by September as it rolled out agentic “software factory” workflows, but the growth depends on building a fast, high-fidelity verification “sensor” so agents don’t outrun what tests can actually validate. In parallel, Anthropic open-sourced its @anthropic-ai/sandbox-runtime, an OS-level sandbox (no containers) that reportedly cut internal Claude Code permission prompts by 84%—a practical nod to how real agent deployments are constrained by risk controls, not ambition.
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