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'MLSecOps embeds security, governance, and monitoring across the ML lifecycle to secure CI/CD workflows and ensure trustworthy, compliant model deployments.'
MIT researchers have developed a method to stabilize large transformer training by enforcing Lipschitz bounds through spectral weight regulation and the Muon optimizer, eliminating the need for traditional normalization techniques.
Master-RM is a new reward model designed to fix vulnerabilities in LLM-based evaluators by reducing false positives caused by superficial cues, ensuring more reliable reinforcement learning outcomes.