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Feb 23, 2024 Back to Basics – REINFORCE for Human Feedback in LLMs Reinforcement learning from human feedback has been widely adopted as a way to ensure models reflect preferences.

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Research Cohere

ResearchCrosslingual Reasoning through Test-Time ScalingCohere ResearchINCLUDE: Evaluating Multilingual Language Understanding with Regional KnowledgeCohere ResearchScalable Training of Language Models using PAX pjit and TPUv4Cohere ResearchFairness of Deep Ensembles: On the interplay between per-group task difficulty andCohere ResearchWhen Less is More: Investigating Data Pruning for Pretraining LLMs at ScaleCohere ResearchIntriguing Properties of Quantization at ScaleCohere BlogsHow MotherDuck Scales DuckDB in the Cloud vertically and horizontallyMotherduck Blogs3 easy ways to add AI Summarization to Conversation Intelligence toolsAssemblyai Blogs2022 at AssemblyAI - A Year in ReviewAssemblyai BlogsFixing Smooth Scrolling with Find-on-PageCss Tricks ResourcesRolling back a production deploymentVercel BlogsNew! Break Transcripts into Paragraphs and SentencesAssemblyai BlogsSocket at BSidesSF and RSA Conference 2023Socket BlogsSocket at Black Hat and DEF CON 2023Socket NewsUpgrading Istio without DowntimeThenewstack LearnText basics | web.devWeb ResourcesRound Trip Time (RTT) - GlossaryDeveloper Mozilla BlogsBack to school: Notion is now free for student organizationsNotion So LearnDuckDB & MotherDuck for Beginners: Your Ultimate GuideMotherduck BlogsGit for Data AppliedMotherduck