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Morning Edition · Wednesday, June 24, 2026
New work examines whether reasoning distillation losses differ in weight space, not just accuracy
A paper asks whether offline RL objectives that transfer reasoning from large teachers to small students produce genuinely different models, beyond their downstream scores.
An arXiv paper on the weight-space geometry of offline reasoning training takes up a question the field usually skips. Offline reinforcement-learning (RL) objectives such as RFT, DFT, offline GRPO and DPO are widely used to distill reasonin…
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- Five Eyes warns of AI cyberattack capability as OpenAI ships a security-tuned model
- US export directive cuts off Fable 5 and Mythos 5, hardening model access as a controlled good
- GPT-5 Pro credited with cracking a three-year immunology problem
- Meta's Muse Spark glasses push perception and translation onto the face
- Anthropic co-founder floats recursive self-improvement by 2028
- New benchmark probes agentic AI for attack surfaces that static tests miss
- Nvidia powers more than 400 of the 500 fastest supercomputers
- Mistral OCR 4 turns documents into structured layouts across 170 languages
- Segment Anything 3 reaches a consumer app, testing open-vocabulary vision in production
- A proposal to give AI agents cryptographic certificates of correctness
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