AI Model
AI & LLM Optimization Using RLHF and SFT — Expert-Driven Solutions
At ACS Networks, we transform raw data into powerful, human-aligned AI and Large Language Models (LLMs). Using advanced Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT) techniques, our experts train models to understand context, behave intelligently, and deliver real-world.We combine human insight with rigorous quality processes to ensure every model becomes smarter, safer, and more reliable. With our end-to-end expertise, we help organizations build scalable AI that grows with their business and amplifies their capabilities. accuracy.
Our Capabilities
Pretraining — Building the Foundation
The process begins by training the model on large-scale text datasets. This stage teaches the model general language understanding, world knowledge, and reasoning patterns. It forms the base intelligence the model needs before being aligned to human expectations.
Supervised Fine-Tuning (SFT) — Teaching the Model to Follow Instructions
Next, the base model is fine-tuned using curated, high-quality human-labeled responses. Experts provide example prompts and ideal answers, allowing the model to learn. This step teaches the model “how humans expect it to communicate.”
Human Preference Collection
Human evaluators compare different model responses and choose the best ones. These preference rankings are used to create datasets showing. This step gives the model a clear signal about what good behavior looks like.
Reward Model Training
Using the preference data collected from humans, we train a reward model. This reward model learns to score outputs based on how aligned they are with human preferences. It becomes the “internal judge” that helps guide the main model.
Reinforcement Learning (RLHF)
We optimize the main model using reinforcement learning.
The model generates responses, the reward model scores them, and the system adjusts itself to improve. RLHF is what transforms the model from “just trained” to human-aligned AI ready for real-world deployment.
Reward Model Training
Using the preference data collected from humans, we train a reward model. This reward model learns to score outputs based on how aligned they are with human preferences.It becomes the “internal judge” that helps guide the main model.