Read our latest blogs and case studies
Deep dive into the latest technologies and methodologies that are shaping the future of Generative AI Fine-tuning
Advanced Human Preference Optimization Training for Generative AI
Advanced Human Preference Optimization Training for Generative AI
RLHF (Reinforcement Learning from Human Feedback)
+ DPO (Direct Preference Optimization)
When AI Lacks Human Context
Large Language Models (LLMs) and multimodal AI systems are incredibly powerful, but by default, they can be generic for specific use cases. They can often:
Miss subtle organizational tone and domain nuances.
Produce hallucinations or biased outputs.
Struggle with regulatory and ethical constraints.
Fail in edge cases where precision and trust are mission-critical.
Businesses need advanced AI that reflects human values, niche intelligence, business context, compliance boundaries, and beyond.
Digital Divide Data’s Human Preference Optimization (HPO)
Our Human Preference Optimization (HPO) solutions use Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) to align AI with human intent. Models optimized with DDD’s HPO achieve safer refusals, consistent brand tone and policy compliance, alignment across multilingual and domain-specific contexts, and more — while also improving task success, strengthening instruction adherence, and reducing unsafe outputs.
Reinforcement Learning from Human Feedback (RLHF)
Our HPO Solutions
Our RLHF approach teaches models to internalize nuanced human preferences for enterprise-ready performance.
Reward Modeling
We train reward models on expert-labeled examples that capture factual accuracy, business tone, and domain-specific quality.
Safety-Guided Policy Tuning
We guide models to stay within ethical and regulatory boundaries, reducing hallucinations, bias, and toxicity while preserving fluency.
Human-in-the-Loop Expertise
Our multilingual domain specialists deliver real-time, granular preference signals that sharpen decision-making in complex scenarios.
Continuous Feedback Loops
Using A/B testing, Likert scoring, and pairwise comparisons, we continuously fine-tune models across modalities, use cases, and demographics.
Direct Preference Optimization (DPO)
Our DPO pipelines apply human preference data directly, enabling rapid, sample-efficient alignment without complex reinforcement learning steps.
Custom Feedback Pipelines
We design tailored feedback flows with clear rubrics and guidelines, ensuring that optimization aligns with your business goals.
Structured Preferences at Scale
We capture rankings, labels, and free-form feedback in formats that directly improve model behavior.
Global Expert Contributions
Our global pool of trained SMEs delivers culturally relevant evaluations across various industries, including finance, healthcare, legal, and the public sector.
Risk and Safety Stress Tests
We stress-test models with targeted challenges and red teaming inputs, surfacing risks in high-stakes content.
Our HPO Workflow
We combine methodological depth with enterprise-grade rigor
1. Scope
We partner with clients to set clear success criteria rooted in business goals.
2. Rubric
Our experts design custom guidelines, taxonomies, and scoring rubrics so every evaluation
3. Data
We collect preference data through structured human feedback, pairwise rankings, Likert scoring.
4. Training
Using your preference signals, we run tailored DPO pipelines for rapid alignment or RLHF
7. Monitor
We stay involved post-deployment, tracking outputs for performance, safety deltas.
6. Deploy
We help you embed the optimized model into enterprise workflows, adding policy-first guardrails
5. Evaluation
We test models with automated harnesses and human reviews, applying explicit thresholds
95%
Drop in flagged responses
Toxicity Reduction Score Evaluates reduction in biased, unsafe, or toxic outputs.
Toxicity Reduction Score Evaluates reduction in biased, unsafe, or toxic outputs.
45%
Boost in F1 Score
Evaluates precision and recall for tasks like summarization, classification, Q&A.
Evaluates precision and recall for tasks like summarization, classification, Q&A
85%
Fewer Hallucinated Responses
Frequency of fabricated or incorrect facts.
Frequency of fabricated or incorrect facts
The DDD Difference
Reliability Framework
Our HPO workflows are powered by advanced domain SMEs who design clear rubrics, calibrate evaluators, and ensure inter-rater reliability so feedback is consistent, precise, and business-aligned.
Governance
Dataset versioning, audit trails, diagnostics, and reproducibility give enterprises the governance needed to deploy AI with confidence.
Platform Agnostic
We are platform-agnostic but build policy-first pipelines, designed for privacy safeguards and compliance controls.
Security
We are SOC 2 Type II certified, follow NIST 800-53 standards, and comply with GDPR, ensuring data is protected, private, and handled with enterprise-grade security.
What Our Clients Say
Our Impact
By pioneering an impact sourcing model, DDD has improved preference coverage across domains and languages, bringing a unique perspective and strengthening alignment quality for human preference optimization solutions.
Discover MoreAlign Gen AI for Human Intent, Business Values, and Scalability
FAQs
-
RLHF utilizes a reward model and iterative tuning, making it an ideal solution for complex, safety-critical use cases where nuance is crucial. DPO skips the reward model and directly optimizes on ranked preferences, offering faster, simpler alignment at scale. Many enterprises combine both DPO for speed and RLHF for depth.
-
DPO can deliver strong results with tens of thousands of ranked examples, while RLHF usually requires hundreds of thousands or more to train stable reward models. At DDD, we strike a balance between efficiency and quality through hybrid strategies that combine human and synthetic data.
-
A standard project runs 8–12 weeks: two to three weeks for scoping and rubric design, four to eight weeks for data collection, and two to four weeks for training and evaluation. Accelerated pilots can show results in as little as six weeks.
-
DDD supports broad multilingual coverage and dialects through global delivery centers and domain-trained evaluators. We design localized rubrics to ensure cultural and linguistic relevance, enabling consistent optimization across regions and modalities.
-
We apply enterprise-grade security with encryption, access controls, and anonymization. For regulated industries, we support on-premise or air-gapped deployments. Subject matter experts are carefully selected and compliance-trained to protect sensitive data at every step.
-
RLHF requires more data, compute, and time but yields robust, safe, and reliable models. DPO is faster and cheaper, though it may need periodic re-optimization. DDD helps clients balance both approaches to maximize ROI and minimize latency.