Langhui Technology's frontier model alignment data services leverage 5,000+ certified domain experts to deliver RLHF preference data, chain-of-thought reasoning traces, SFT supervised fine-tuning data, and adversarial red teaming data for GPT-, Claude-, and Gemini-class frontier LLMs — empowering models to achieve value alignment, safety alignment, and capability breakthroughs.
Tracking the alignment research roadmaps of OpenAI, Anthropic, Google DeepMind, DeepSeek, and Meta, Langhui's expert team continuously translates the latest academic breakthroughs into production-grade, high-quality alignment data at scale.
A new 2026 paradigm for CoT reasoning alignment. Through long-chain-of-thought trace annotation and Process Reward Model (PRM) data, we align the reasoning paths of o3-class reasoning models, improving accuracy and interpretability on complex multi-step reasoning tasks.
An upgrade to Constitutional AI based on the Claude Constitution. Through rule supervision and self-critique data production, models adhere to interpretable value principles without relying on massive human annotation.
A breakthrough in aligning open-source reasoning models. Leveraging Group Relative Policy Optimization (GRPO) and Reinforcement Learning with Verifiable Rewards (RLVR) data, open-source models achieve closed-source frontier performance on math, code, and logical reasoning.
The 2026 evolution of alignment methods. Direct preference optimization methods such as DPO, IPO, and KTO are progressively replacing traditional RLHF in production — aligning models without training a separate reward model and substantially reducing engineering cost and training instability.
Multilingual value alignment across 200+ languages. Using culturally localized preference data, we close the alignment gap for low-resource languages, helping open-source models maintain value consistency across cross-cultural scenarios.
Red Teaming 2.0 upgraded. Through coordinated attacks combining automated red teaming with expert red teaming, we cover jailbreaks, prompt injection, value drift, and other safety risks — building a defensive perimeter for frontier models.
Four core capabilities span the full lifecycle of frontier model alignment — from preference collection to red team attacks — forming a production-grade, auditable, and fully traceable alignment data pipeline.
5,000+ certified domain experts deliver high-quality human preference feedback across medicine, law, finance, education, code, and other professional domains. Supports Pairwise, Likert, and Best-of-N preference formats.
Multi-step Chain-of-Thought reasoning annotation across math, code, logic, and science scenarios. Supports dual-track labeling with Process Reward Models (PRM) and Outcome Reward Models (ORM).
High-quality instruction-demonstration data covering general dialogue, knowledge Q&A, tool calling, and agentic tasks. Supports multi-turn, multimodal, and multilingual formats.
Safety boundary testing and adversarial sample production, executed jointly by safety experts and automated red teaming tools. Covers jailbreaks, prompt injection, value drift, harmful outputs, and other risk categories.
Six standard off-the-shelf alignment datasets covering preference, reasoning, supervision, safety, multilingual, and value dimensions. Delivered via subscription or fully customized engagement.
Pairwise preference data spanning general dialogue, professional Q&A, creative writing, and code generation — each entry annotated by 3+ independent experts.
Multi-step chain-of-thought annotations for math, code, logic, and scientific reasoning — including process reward labels and intermediate step correctness judgments.
High-quality instruction-response demonstration data covering multi-turn dialogue, tool calling, agentic tasks, and knowledge Q&A.
Adversarial sample library covering jailbreaks, prompt injection, value drift, and more — including successful attack cases and defensive remediation recommendations.
Preference and value alignment data across 200+ languages — including Chinese, English, Japanese, Korean, French, German, Spanish, Arabic, and more — with culturally localized annotation.
Harmful content identification, refusal policies, and value sensitivity labeling — helping models respond compliantly in sensitive scenarios.
From general-purpose foundation models to vertical domains, from reasoning models to multimodal systems — Langhui's alignment data services fit the full spectrum of frontier model alignment needs.
Massive preference and SFT datasets for GPT-, Claude-, and Gemini-class foundation models — improving Helpfulness, Honesty, and Harmlessness (the HHH principles).
Expert-grade alignment data for healthcare, legal, finance, and education industry LLMs — ensuring accuracy, compliance, and trustworthiness in professional scenarios.
CoT trace data and process reward data for o3, DeepSeek-R1, QwQ, and other reasoning models — boosting performance and interpretability on complex reasoning tasks.
Cross-modal preference data and safety alignment data for image-text, video, and audio multimodal LLMs — ensuring alignment consistency across multimodal interactions.
Langhui has built a quality assurance system that spans the entire data production lifecycle — from expert credentials to preference consistency, reasoning correctness, and safety coverage. Every dimension is backed by quantifiable KPIs.
Partner with Langhui's 5,000+ certified experts to infuse your model with the latest 2026 alignment methods. Whether you're a foundation model team or a vertical application team, we deliver alignment data solutions tailored to your needs.