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Model Training

276 summarised stories about Model Training, each linking back to the original source. Browse all topics →

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Thursday, 1 October 2026

Always-on AI agents turn infrastructure into a continuous learning loop

SiliconANGLE 2 weeks ago 26 ● 9 sources

Cognition AI used always-on AI agent infrastructure to connect software development work with continuous inference, feedback, and training so Devin can improve from production outcomes. The effort targets 99.99% reliability for distributed training across thousands of GPUs, and Nvidia’s early access to the Vera Rubin platform supports better price-performance research. CoreWeave Forge adds tracing, distillation, and RL rollout services that can hot-load updated checkpoints into live deployments, changing the workflow from occasional retraining to an ongoing learning loop.

Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs

Hugging Face 2 weeks ago 40 ● 3 sources

Olmo released Olmo-core 3, an open training framework with a redesigned mixture-of-experts system for scaling large language-model training. In one benchmark, it increased the expert pool from 8 to 128 while keeping about 3.2B active parameters per token and limiting throughput drop to under 5%. It replaces an earlier FSDP-based MoE approach with a DDP-style expert-resident training stack (and other routing/precision optimizations) to improve throughput and enable scaling up to trillion-parameter MoE configurations.

Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs

Allen Institute (AI2) 3 weeks ago 19 ● 3 sources

Olmo-core 3 was released as an upgraded open training framework for large mixture-of-experts (MoE) language models. In benchmarks, increasing the expert pool from 8 to 128 while keeping 4 experts selected per token held active parameters per token to about 3.2B and reduced training throughput by less than 5%. The update changes the training stack by switching from FSDP to a DDP-based approach that keeps experts on GPUs and adds routing, throughput, and MXFP8 optimizations to scale efficiently toward trillion-parameter MoEs.

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