AINewsnow

Can Representation Learning Decouple from Loss Minimization? Polar Updates Have an Answer

arXiv:2609.36240v1 Announce Type: cross Abstract: Does representation learning stop when the training loss stops improving? We study this question for matrix Muon, whose polar-normalised updates have a step length set by the gradient's rank rather than its norm. Near the edge of stability, full-bat…

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-09-30 04:00 · arXiv stat.ML
    Can Representation Learning Decouple from Loss Minimization? Polar Updates Have an Answer

More stories

  1. NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring — NVIDIA Technical Blog
  2. The Future Is for Everyone: Muse for Small Business — Meta Newsroom
  3. How we found 24 Android vulnerabilities using our open source AI security agent — GitHub Blog
  4. Introducing Claude Sonnet 5.5 on AWS — AWS Machine Learning Blog
  5. OpenAI launches Dots, its Muse competitor — The Verge AI
  6. OpenAI pauses AI training, launches ‘extensive’ review after multiple rogue agent incidents — Mint AI
  7. Anthropic warns of ‘existential risks to humanity’ in IPO prospectus — Financial Times AI
  8. OpenAI Scraps Release of New AI Model Over Safety Concerns — Wall Street Journal Technology

Get the daily brief of stories like this at 6:30 every morning →