SDG Hub: Building synthetic data pipelines with modular blocks
SDG Hub is an open framework for building, composing, and scaling synthetic data pipelines with modular blocks for LLM training.
SDG Hub is an open framework for building, composing, and scaling synthetic data pipelines with modular blocks for LLM training.
Customize reasoning models with synthetic data generation for enterprise deployment. Learn techniques from Red Hat's AI Innovation Team.
Discover inference-time scaling techniques that improve AI quality and reliability for enterprise applications beyond just speed optimization.
Introducing Async-GRPO - an open-source library for scalable reinforcement learning with 42% efficiency gains over VERL and 11x over TRL for GRPO training.
Learn how our adaptive SVD method enables continual learning in LLMs with near-zero catastrophic forgetting, achieving 7% higher accuracy than baselines.
Understanding the distinction between reasoning and inference-time scaling in LLMs - insights from our R1 reproduction experiments.
Second update on R1 reasoning research - new results on training small LLMs with synthetic reasoning data and particle filtering methods.
First update on R1-like reasoning experiments - Granite models show significant gains with particle filtering and new data quality experiments.
Learn how to reproduce R1-like reasoning in small LLMs using particle filtering, synthetic data, and GRPO - achieving GPT-4o accuracy with only 4 rollouts.