
Research
How to Train a SOTA Frontier Decision Model
Discover how Fastino trained GLiDE v1.2, a state-of-the-art decision model, using reasoning distillation, improved training data, and multi-teacher learning to boost accuracy and reduce latency.

Guide
What is RLCD? The Training Method Behind Decision Models
Learn what reinforcement learning for calibrated decisions is, how it works, and when you should use it for your models.

Research
Big Model, Small Adapter: Fine-Tuning NVIDIA Nemotron 3.5 Super VL
See how LoRA fine-tuning NVIDIA Nemotron 3.5 Super VL improves finance and healthcare benchmarks while reducing output tokens and inference costs.

Product
Introducing the Fastino Training API: An Open Weight Models Training API for Supervised Fine-Tuning and Reinforcement Learning
Run supervised fine-tuning and reinforcement learning with the Fastino Training API, featuring managed GPUs, per-token pricing, and Tinker SDK compatibility.

Research
A Practical Guide to Harness Engineering
Learn harness engineering for AI agents: design tools, manage context and memory, enforce constraints, and benchmark accuracy, latency, and cost.

Guide
Decision Models Explained: Use Cases and Top Model Comparison
Learn what decision models are, how they differ from classifiers, and when to use them. Compare top models, including open-weight and trainable options.

Guide
Fine-Tuning vs RAG vs Prompt Engineering: How to Adapt an Open Weight Model
A practical guide to choosing between prompt engineering, RAG, supervised fine-tuning (SFT and LoRA), continued pre-training, preference optimization (including DPO), and reinforcement learning.

Product
Introducing GLiDE: The First Thinking Decision Model
Introducing GLiDE - the first thinking decision model built for difficult, structured decisions.

Product
GLiNER2.5-Decide: An Open-Weight Model for Structured Decision Making
Introducing GLiNER2.5-Decide, a new classification encoder model fine-tuned for constrained decision making.
Read our published research, written by our technical team, from new model releases to product research.
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