Post Training Methods Language Models
Post-training adapts language models for specific, safe, and practical uses. This overview highlights key methods and the open source training_hub library.
Blog
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.
Getting reasoning models enterprise ready
Customize reasoning models with synthetic data generation for enterprise deployment. Learn techniques from Red Hat's AI Innovation Team.
Beyond tokens per second: Unlocking smarter enterprise AI with inference-time scaling
Discover inference-time scaling techniques that improve AI quality and reliability for enterprise applications beyond just speed optimization.
Async-GRPO - Open, Fast, and Performant
Introducing Async-GRPO - an open-source library for scalable reinforcement learning with 42% efficiency gains over VERL and 11x over TRL for GRPO training.
Sculpting Subspaces: How We Solved Continual Learning in Large Language Models
Learn how our adaptive SVD method enables continual learning in LLMs with near-zero catastrophic forgetting, achieving 7% higher accuracy than baselines.
Update 3 - On Reasoning vs Inference-time scaling - Lessons on Reproducing R1-like Reasoning in Small LLMs without using DeepSeek-R1-Zero (or its derivatives)
Understanding the distinction between reasoning and inference-time scaling in LLMs - insights from our R1 reproduction experiments.
Update 2 - Lessons on Reproducing R1-like Reasoning in Small LLMs without using DeepSeek-R1-Zero (or its derivatives)
Second update on R1 reasoning research - new results on training small LLMs with synthetic reasoning data and particle filtering methods.
Update 1 - Lessons on Reproducing R1-like Reasoning in Small LLMs without using DeepSeek-R1-Zero (or its derivatives)
First update on R1-like reasoning experiments - Granite models show significant gains with particle filtering and new data quality experiments.