RLHF Services: Human Feedback for AI Model Alignment
Ensure your generative AI models behave in ways that are helpful, safe, and aligned with human values.
LXT’s RLHF services deliver high-quality, scalable human feedback for fine-tuning reward models – enabling more accurate, ethical, and user-aligned AI behavior.
Why leading AI teams choose LXT for RLHF services
Expert human preference judgments
Our global workforce ranks and reviews AI outputs using structured RLHF frameworks – pairwise comparisons, Likert scales, or open feedback.
Cultural, linguistic & domain fit
With over 250K experts in 100+ fields and coverage across 1,000+ language locales, we ensure feedback reflects your target audience and use case.
Custom reward model pipelines
We design workflows tailored to your alignment goals – whether optimizing for helpfulness, harmlessness, coherence, or task-specific intent.
Transparent scoring & analytics
Every decision is backed by calibrated guidelines, consensus logic, and real-time reporting to help you measure alignment progress.
Secure, enterprise-grade delivery
ISO 27001 certified, SOC 2 compliant, and available via secure facilities for confidential model outputs or proprietary datasets.
Scalable evaluations for GenAI training
From 1K to 1M+ RLHF samples – LXT delivers fast, consistent human feedback to support high-volume reward model optimization at scale.
LXT for RLHF Services
Reinforcement Learning from Human Feedback (RLHF) is a critical step in aligning generative AI with human expectations.
Whether you're optimizing a reward model for helpfulness or preventing unsafe outputs, LXT provides the expert feedback and scalable infrastructure you need to train responsible, real-world-ready AI.
Our human evaluators combine cultural awareness, domain knowledge, and task-specific training to deliver reliable feedback at every scale.
From experimental research to production-scale fine-tuning, we help you measure and shape model behavior through structured, high-quality human input.
Our RLHF services include:
From ranking model responses to collecting nuanced human feedback, we deliver structured evaluation workflows that support every stage of your RLHF pipeline.
Pairwise output ranking
Evaluate model responses in A/B format to identify preferred outputs – essential for training reward models via PPO or similar algorithms.
Scaled preference scoring
Apply Likert-style rating scales to capture nuance in output quality, coherence, or helpfulness across large volumes.
Open-ended feedback collection
Gather qualitative insights from human annotators on tone, fluency, factuality, and safety to refine model behavior.
Task-specific alignment evaluation
Design judgment criteria tailored to your use case – e.g., instructional accuracy, response safety, or refusal robustness.
Multilingual feedback workflows
Support RLHF evaluations across global languages and regions to ensure cultural fit and global applicability.
Reward model training data output
Deliver structured, high-agreement datasets ready for direct use in reinforcement fine-tuning pipelines.
How our RLHF project process works
Every RLHF project at LXT follows a structured, collaborative workflow – combining expert workforce management, calibrated guidelines, and secure infrastructure to deliver consistent, high-quality human feedback at scale.
We work closely with your team to define model objectives, evaluation criteria, task types, and expected output – ensuring full alignment with your alignment goals and technical requirements.
Our experts design a custom feedback pipeline tailored to your use case. We select and train qualified reviewers based on target language, domain expertise, and cultural context.
Using gold tasks and test runs, we refine instructions and calibrate reviewer agreement – ensuring consistent, high-quality judgments before full-scale launch.
Human feedback tasks are deployed through LXT’s secure internal platform – supporting pairwise ranking, scaled scoring, or open-ended reviews at high volumes.
We apply multi-layer QA checks including overlap scoring, audit sampling, and reviewer performance tracking—maintaining reliability across the dataset.
You receive structured output in your preferred format—fully anonymized, version-controlled, and ready for integration into your reward model training pipeline.
As your models evolve, we support new task variants, updated criteria, and scaled feedback collection—enabling continuous improvement and safe deployment.
Secure services for RLHF projects
RLHF projects often involve sensitive model outputs, confidential prompts, or regulated data. At LXT, security is built into every step of the process.
We operate under ISO 27001 and SOC 2 certifications, with strict access controls, encrypted infrastructure, and audit-ready workflows. For highly sensitive projects, we offer secure facility execution by vetted staff.
All data is anonymized, versioned, and handled under mutual NDAs with role-based access. Our team also supports custom compliance workflows to meet your legal, regulatory, or internal requirements.
Industries & use cases for RLHF services
LXT’s RLHF services support organizations developing generative AI systems that must align with human expectations, safety standards, and real-world behavior.
We work across sectors where accuracy, intent alignment, and cultural sensitivity are critical.
Technology & Generative AI
Train reward models to improve helpfulness, reduce hallucinations, and align LLM behavior with human preference.
Media & Social Platforms
Evaluate content moderation responses, chatbot tone, and alignment with community guidelines.
Finance & Insurance
Ensure AI-generated responses in customer-facing tools reflect compliance, clarity, and responsible communication.
Healthcare & Life Sciences
Support safe, culturally sensitive model tuning in high-risk domains such as diagnostics, summaries, or virtual assistants.
Public Sector & Legal
Help refine models used in decision support, citizen interaction, and legal reasoning with unbiased human feedback.
Automotive & Robotics
Train systems that respond accurately to human instructions or environmental cues – enhancing safety and interpretability.
Further validation & evaluation services
RLHF is one part of building safe, effective, and trustworthy AI systems.
LXT offers a full suite of validation and evaluation services to support every stage of your AI lifecycle.
AI data validation & evaluation
Explore all our validation and evaluation solutions in one place – covering both training data quality and model performance across the AI lifecycle.
AI training data validation
Verify that your datasets are complete, balanced, and bias-free before model training begins.
Search relevance evaluation
Measure how effectively your AI ranks and retrieves results based on real user intent.
AI model evaluation
Assess model outputs for accuracy, fairness, safety, and cultural relevance across modalities and languages.
Human in the loop
Integrate expert human feedback into live systems for ongoing tuning, safety, and performance control.
FAQs on our RLHF services
Reinforcement Learning from Human Feedback (RLHF) is a technique for fine-tuning AI models using structured human judgments—typically on output quality or preference—to improve alignment with human expectations.
We support pairwise ranking, scaled scoring, open-ended feedback, and task-specific evaluations—delivered through structured workflows and expert reviewers.
Yes. We operate under ISO 27001 and SOC 2 certifications, and offer secure facility execution for projects involving confidential or regulated data.
We use gold tasks, reviewer calibration, overlap scoring, and continuous audits to ensure consistency and reliability across all evaluations.
We can typically launch a scoped RLHF pilot within a few business days, depending on task complexity, reviewer language needs, and data volume.