Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences
Chinese Academy of Sciences
Humans in the loop, with a simple API call.A reward you can't hack.
A reward you can't hack.
Humans.
API-access to 8K+ high-quality annotations per minute to enable online RLHF, refine model behavior, and evaluate outputs.
01
Install the SDK
Use our Python SDK and integrate with your existing data pipelines in just a few lines of code.
Check the docs02
Define your task
Create a Job Definition with your inputs and specify the feedback you need (comparison, classification, ranking), along with instructions, context, and key parameters.
What are audiences?03
Distribute it
Tasks are instantly routed to targeted audiences, by language, geography, or task-specific expertise (e.g. native speakers, prompt alignment, artifact detection), generating thousands of high-quality annotations per minute.
Results in minutes instead of weeks04
Get results
Results are returned via API as annotated datapoints, preference data, or rankings that are ready for direct integration into model pipelines.
Humans in the training loopOur annotators in action
0Active annotators
0Total annotations to date
0Target countries
Live sessions happening right now on our system.
Run pairwise comparisons, model ranking, and multi-dimensional scoring (e.g. quality, alignment, realism) on model outputs.
Build benchmarks, compare versions, and track performance over time.
Why choose Rapidata?
Speed
Human feedback at GPU speed
Tasks are distributed programmatically across millions of contributors, generating millions of responses per day. Real-time RLHF workflows reach 8K+ annotations / min with near-zero latency.
Unbiased feedback
Diverse, incentive-aligned feedback
Global distribution avoids siloed workforces. Voluntary participation reduces incentive bias and produces more natural, preference-aligned responses.
Cost efficiency
Pricing that scales with you
Starting at $4 per 1,000 responses, pricing scales with throughput and task complexity. Transparent and predictable, with context and audience targeting influencing cost.
Data quality
High-quality human feedback
Annotators are pre-screened and continuously scored using validation tasks. Task-specific audiences ensure consistent, high-quality signal across annotations.
Trusted, but often not allowed to disclose
Rapidata helps us test our voice models with real users in real contexts worldwide — in days, not months.
Lily CliffordCEO of Rime
Rapidata not only helps us evaluate the model at scale, but also informs inputs into the model, that help us remain best in class.
Viren TellisCEO of Uthana
Rapidata combines the best of both worlds: automation for efficient evaluation and human insight for real quality assessment. The result is fast, reliable model evaluations you can actually trust.
BertrandCEO of Pruna
@ Top-tier Image lab
[...] you are so precious to us that we can't share it with our competitors.
Lead Researcher (ordering 100M+ annotations / month)
Peer-reviewed research building on Rapidata
UnifiedReward-Think: Unified Multimodal Chain-of-Thought Reward Model through Reinforcement Fine-Tuning
Tencent Hunyuan · Fudan University · Shanghai AI Lab
Factuality Matters: When Image Generation and Editing Meet Structured Visuals
CUHK MMLab · Hugging Face · ByteDance +3
PICABench: How Far Are We from Physically Realistic Image Editing?
Shanghai AI Lab · CUHK MMLab · Krea AI +2
Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
UT Austin
SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models
Fudan University · University of Adelaide
Accelerating Masked Image Generation by Learning Controlled Latent Dynamics
Shanghai AI Lab · Shanghai Jiao Tong University
MSRL: Scaling Generative Multimodal Reward Modeling via Multi-Stage Reinforcement Learning
ByteDance · Northeastern University
FakeXplain: Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs
Shanghai Jiao Tong University · Ant Group
VR-Thinker: Boosting Video Reward Models through Thinking-with-Image Reasoning
Kuaishou Kling · CUHK MMLab · Nanjing University
PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos
NVIDIA · University of Waterloo · Vector Institute
Plan-and-Verify Video Reward Reasoning with Spatio-Temporal Scene Graph Grounding
Korea University
Latest insights & updates
Subscribe to our Newsletter
Subscribe and never miss an update!Be the first to know whenever something new arrives.
Some of our partners
FAQ
Can't find the answer you're looking for?Contact our support team at info@rapidata.ai!
Rapidata provides an ‘API to humans’. That is, we offer an easy and extremely fast solution to get feedback and responses from across the globe. Get the first responses in a matter of seconds, with the possibility of over 300,000 responses per hour. This is useful for data-labeling, preference data, model evaluation, output validation, etc.