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Reinforcement Fine Tuning: Train expert open models to surpass closed frontier models

Fireworks

We’re excited to announce the beta release of Reinforcement Fine-Tuning (RFT), a powerful new technique to create expert models for complex tasks across agentic reasoning, function calling, coding, and more.

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Blogs Fireworks

BlogsTrilogy Validates Open-Weight AI Models for Enterprise Workloads with FireworksFireworks BlogsDeep-Dive into LLM Fine-TuningFireworks BlogsPartnering with Meta: Bringing Llama 3.2 to Fireworks for Fine-Tuning and InferenceFireworks BlogsUnlock Advanced Reasoning with NVIDIA Nemotron Nano 2 Models on FireworksFireworks BlogsHow we fixed prompt injection for all models on FireworksFireworks BlogsFireFunction V1 - Fireworks’ GPT-4-level function calling model - 4x faster than GPT-4 and openFireworks LearnModel Types and PerformanceVercel BlogsFast AI Feedback Loops with Honeycomb and OpenTelemetryHoneycomb BlogsHoneycomb Acquires Grit and Expands Leadership Team to Accelerate Customer Value and EnterpriseHoneycomb Products & ServicesGitBook & Scalar: LetGitbook Products & ServicesWhat is OKF? Understanding Google’s Open Knowledge FormatGitbook Products & ServicesSpeech Understanding APIAssemblyai ResearchCritical Learning Periods: Leveraging Early training Dynamics for Efficient Data PruningCohere ResearchThe Art of Asking: Multilingual Prompt Optimization for Synthetic DataCohere BlogsCohere Labs Launches Tiny Aya for Multilingual AI | CohereCohere Products & ServicesModel Vault | Dedicated Model Inference Platform | CohereCohere 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 ResearchElo Uncovered: Robustness and Best Practices in Language Model EvaluationCohere ResearchFrom One to Many: Expanding the Scope of Toxicity Mitigation in Language ModelsCohere