Custom reward functions for multi-turn reinforcement learning with Amazon Nova Forge
An AWS blog post details how to build custom multi-turn reward functions for Amazon Nova Forge, run model-generated code safely, and instrument reward components.
#fine-tuning
11 articles tagged with “fine-tuning”.
Writer introduces new AI model and upgraded harness to contain token costs
Writer introduced a new AI model and an upgraded harness designed to contain token costs, built as a post-training variation on Z.ai's open source GLM-5.2 model.
How ONESTRUCTION built the Ishigaki-IDS foundation model with AWS GenAIIC
ONESTRUCTION, advised by the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model for construction and BIM workflows using synthetic data and a three-stage training pipeline on Amazon EC2.
Orchard: An open framework for scalable agentic AI
Microsoft Research has released Orchard, an open-source framework for training and evaluating AI agents across task types, aiming to reduce complexity and support smaller models.
Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova
An AWS post introduces Self-Distilled Reasoning (SDR), an approach for adding reasoning traces to supervised fine-tuning datasets that lack them, validated across three benchmarks with Amazon Nova.
NVIDIA Vera Rubin Maximizes Intelligence per Dollar for Post-Training Workloads — a Key Metric for Age…
NVIDIA's Vera Rubin is positioned to reduce cost per token for post-training workloads through codesign, maximizing intelligence per dollar for agentic AI.
Nemotron Labs: How Open Models Give Enterprises and Nations AI They Can Trust, Control and Customize
NVIDIA's Nemotron Labs highlights open models as a way for enterprises and nations to build AI they can trust, control and tailor to domain-specific needs.
LoRA and Efficient Fine-Tuning, Explained
Fine-tuning a large model used to mean owning a data centre. LoRA changed that by training a tiny fraction of the weights. Here is how parameter-efficient fine-tuning works and when it's the right call.
Deploying quantized models on Amazon SageMaker AI with Unsloth
An AWS blog post explains four deployment patterns for serving Unsloth-quantized models on AWS infrastructure, covering EC2, SageMaker AI endpoints, EKS, and ECS.
What Is Fine-Tuning? Customizing AI Models, Explained
Fine-tuning takes a general-purpose AI model and specializes it for your task, tone, or format by training it further on your examples. Here is when it helps — and when prompting or RAG is the better tool.
LoRA: The Trick That Made Fine-Tuning Affordable
Fine-tuning a giant model used to require giant resources. LoRA showed you can adapt one by training a tiny number of extra parameters — and it became the default method for customizing open models.