Blogs
Reinforcement learning: Why alignment of numerics and MoE routing matter
Frontier training requires co-optimized training and inference: Fireworks develops and validates the trainer and rollout engine together so teams can scale reinforcement learning with alignment across numerics, kernels, and MoEs.
ResearchBAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of ExpertsCohere
ResearchAdaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?Cohere
ResearchThe Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce HarmCohere
BlogsState of Agentic AI Report: Key FindingsDocker
BlogsLearning JavaScript with Free Code CampNetlify
BlogsTiny Screens, Big Impact: The Forgotten Art Of Developing Web Apps For Feature PhonesSmashingmagazine
BlogsMuseum Hack Uses Automated Workflows with Base CRM to Leave Manual Work BehindZapier
ResearchMetadata Archaeology: Unearthing Data Subsets by Leveraging Training DynamicsCohere
ResearchContrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionCohere
ResearchInvestigating Continual Pretraining in Large Language Models: Insights and ImplicationsCohere
NewsIstio Brings Future Ready Service Mesh to the AI Era with New Ambient Multicluster, Gateway APICncf
Products & ServicesLlama 4 Scout 17B 16E Instruct API & PricingVercel
