The RLHF Preferable Data, CoT Logic Track, SFT Oversaw fine-tuning of data and adversarial Red Team testing data, supported model value matching, security alignment and capability leaps for GPT, Claude, Gemini Frontline Models.
Checked against public model notes and system cards from July–September 2026: OpenAI GPT-5.6, Anthropic Claude Opus 5.5, DeepSeek V4, and the Gemini 3 line. Langhui turns those shifts into producible alignment data — preferences, long traces, process review, and red-team sets — without presenting lab benchmark scores as our own results.
Mapped to the July 2026 GPT-5.6 family (Sol / Terra / Luna). Alignment data shifts to long-horizon agent workflows, tool-call traces, and process rewards on parallel reasoning paths.
Mapped to the September 2026 Claude Opus 5.5 system card. Data covers constitutional self-critique, long-running coding and computer-use boundaries, and refusal labels for biology and cybersecurity.
Mapped to the DeepSeek V4 / V4.1 open-weight line from summer–autumn 2026. Preference pairs support verifiable rewards (math, code execution) and group-relative policy optimization.
2026 post-training is not a single RLHF recipe. Production mixes DPO, IPO, KTO, GRPO, and verifiable rewards. Langhui delivers the matching preference pairs, process scores, and executable checks.
Mapped to the Gemini 3 Flash / Pro preview cadence. Multimodal sets cover image-text instruction following, long-context consistency, and cross-lingual value consistency, including lower-resource languages.
Mapped to pre-release red-team practice at the frontier labs. Automated plus expert attacks cover jailbreaks, prompt injection, tool misuse, and long-horizon value drift, with severity labels and fix notes.
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.
5000+Accreditors provide high-quality feedback on human preferences covering such specialized fields as medicine, law, finance, education, code, and support Pairwise, Likert, Best-of-N preferences.
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 GPT-5.6, DeepSeek V4, Claude Opus 5.5, 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.
With the Ran-e-Pertificate Expert, you can inject the latest 2026 alignment method into your model. Whether you are a base model team or a vertical application team, we can provide you with a custom-made alignment scheme.