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OpenAI halts frontier-model training amid string of agent misalignment incidents

US Government websites among "dozens of third parties" OpenAI has recently notified.

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Ars Technica Sep 28, 2026, 16:43 UTC
EN

Comparing INT4 and NVFP4 Palettes on Real Gradient Tensors

Four-bit training quantizes every number to one of 16 values. NVFP4's menu is {0, ±0.5, ±1, ±1.5, ±2, ±3, ±4, ±6} , with one scale factor per block…

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Dev.to Aug 22, 2026, 14:00 UTC
EN

Context window sizing for fine-tuning: how long should your training examples be?

Originally published on Loop & Retry — field notes on building LLM agents that survive production. Most fine-tuning guides answer "how many exam…

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Dev.to Aug 8, 2026, 05:36 UTC
EN

Anthropic will design its own hardware to power Claude

Anthropic and OpenAI are racing to scale up while reducing dependence on Nvidia.

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Ars Technica Aug 6, 2026, 20:03 UTC
EN

Faster AI training by quietly cloning the model

A new paper introduces a method to speed up reward-based fine-tuning by having the model generate a cheap, compressed copy of itself to draft text,…

rlposttrainingefficiencytraining
Dev.to Jul 1, 2026, 19:09 UTC
EN

A 35-billion-parameter agent that punches like a trillion-parameter model

A 35-billion-parameter model called Agents-A1 matches trillion-parameter models on multi-step agent tasks, according to a new paper from Shanghai AI…

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Dev.to Jul 1, 2026, 16:05 UTC
EN

LLMs believe false statements even after explicit warnings that they're false

Fine-tuning tests show "bias ... toward confidently representing the claims as true."

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Ars Technica May 28, 2026, 21:29 UTC
RU

GymStats — писал для себя, открыт для всех, кто ходит в спортзал

Был я как-то очередной раз в спортзале: делал упражнения, поглядывая на предыдущие значения из заметок и записывая новые туда же. Придя домой, я обн…

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Habr May 19, 2026, 02:17 UTC
EN

Anthropic blames dystopian sci-fi for training AI models to act “evil”

But training on "synthetic stories" that model good AI behavior can help.

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Ars Technica May 13, 2026, 16:31 UTC
EN

Study: AI models that consider user's feeling are more likely to make errors

Overtuning can cause models to "prioritize user satisfaction over truthfulness.”

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Ars Technica May 1, 2026, 22:23 UTC

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