[ RLHF & Preference Learning ]
Align Model Intelligence with Human Values
Go beyond correctness. Master helpfulness, safety, and nuance with expert-ranked comparison data at scale.
Build your reward model
[ The Alignment Gap ]
Accurate is not always “Right”
SFT models can be factually correct yet unhelpful, verbose, or unsafe.
To bridge the gap between a raw model and a world-class assistant, you need to teach the model how to choose between multiple valid paths.
The Solution? High-density preference pairs that provide the clear signal necessary for Reward Modeling (RM) and Policy Optimization (PPO/DPO).
Reward Hacking
Models finding shortcuts that satisfy metrics but fail users.
Safety Alignment
Defining the razor-thin margin between helpfulness and harm.
Reasoning Nuance
Identifying logical fallacies that simple SFT might overlook.
[ Why Rise Data Labs ]
Expert Judgment, Not Just Labeling
Preference learning requires nuanced human judgment that automated tools simply cannot provide.
01
Nuance-First Rankings
Comparison data built on "the why," not just "the what."
- Expert Rationales Every ranking includes detailed justification.
- Ties & Nuance Subtle differences automated tools miss.
02
Adversarial Safety
Pushing your model to its limits to define safe operational boundaries.
- Red-Teaming Experts provoke toxic responses for negative pairs.
- Edge Cases Low-frequency, high-risk scenario focus.
03
Framework Ready
Data formatted for modern alignment techniques.
- DPO & PPO Ready Structured Parquet/JSONL for immediate training.
- Custom Taxonomy Aligned to your safety guidelines.
[ Capabilities ]
Preference Learning Capabilities
Type
Description
Use Case
Pairwise Ranking
Comparing two model outputs (A vs B) based on custom multi-dimensional rubrics.
Deciding which creative writing response is more engaging and concise.
Chain of Thought
Ranking the logical steps and reasoning process, not just the final answer.
Ensuring a math model didn't get the right answer through a "lucky" logical error.
Multi-Turn Preference
Preference labeling across entire conversation threads for long-term coherence.
Maintaining persona and context over a 10-turn customer support interaction.
Constitutional AI
Critiquing and revising responses against a set of "principles" or a constitution.
Aligning an enterprise model to corporate ethics and legal compliance standards.
[ How It Works ]
The Alignment Pipeline
01
Taxonomy Design
We define what "Good" looks like for your model, creating a rubric for helpfulness, honesty, and harmlessness.
02
Prompt Sampling
We generate diverse prompt sets or use your production logs to create representative evaluation scenarios.
03
Expert Labeling
SMEs rank model outputs, provide rationales, and flag safety violations with high inter-annotator agreement.
04
Iterative Tuning
We provide data in batches, allowing you to train your RM and sample new outputs for the next RLHF round.