Reinforcement Learning with Human Feedback (RLHF)

Overview: Our RLHF service combines expert human feedback with reinforcement learning techniques to fine-tune large language models effectively. By integrating high-quality human annotations, reward signals, and correction data, we help improve model alignment with desired behaviors, enhance safety, reduce hallucinations, and increase factual correctness across use cases. How it works: Submit model outputs, prompts, or training data through S3 or API integration. Our process includes: Designing detailed annotation rubrics for reward feedback Training human annotators to provide quality judgments and corrections Generating reward models from annotated datasets Creating fine-tuning datasets with iterative human feedback loops Providing consensus adjudication and automated fact checks Delivering granular dataset annotations, metrics reports, and remediation suggestions Deliverables: Fine-tuning datasets with reward labels and human feedback annotations (JSONL, CSV) Summary reports with alignment, safety, factuality, and hallucination statistics Playbooks for deploying RLHF pipelines and instruction tuning Audit logs ensuring traceability of human-in-the-loop interventions Quality & Metrics: We report key model improvement metrics, including alignment scores, safety flag rates, factual accuracy, inter-annotator agreement, and feedback impact analysis. Customizable quality thresholds and success criteria are available. Integrations & Formats: Supports standard ML data formats compatible with SageMaker Ground Truth and common machine learning pipelines. Seamless integration with AWS S3, SageMaker, and orchestration APIs (REST, webhook). Security & Compliance: Contractually enforced data privacy with encrypted storage, strict access controls, and secure lifecycle management. Engagement Models: Offers one-time dataset generation, iterative human feedback cycles, or fully managed ongoing RLHF services with routine rubric updates, dashboards, and premium support*