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.
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Check the docsDefine your task
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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 weeksGet results
Results are returned via API as annotated datapoints, preference data, or rankings that are ready for direct integration into model pipelines.
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Answer a task, see what people said
Real datapoints from our open datasets.
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.
Rapidata not only helps us evaluate the model at scale, but also informs inputs into the model, that help us remain best in class.
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.
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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
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FAQ
Can't find the answer you're looking for?Contact our support team at info@rapidata.ai!
Rapidata is a data labeling and human evaluation service that collects labels, ratings, preferences and rankings from over 32 million vetted people in 190+ countries through one API. Get the first responses in a matter of seconds, with the possibility of over 300,000 responses per hour.
Rapidata is for AI teams that train or evaluate image, video, audio, text-to-speech and LLM models and need human data labeling, preference data for RLHF, or human evaluation in hours instead of weeks. It specializes in building datasets for pretraining, direct human-in-the-loop RLHF, and model checkpoint evaluation and ranking.
Rapidata is not built for long expert annotation that needs credentialed specialists (such as medical or legal review) or for hour-long surveys; it is built for short tasks that many people can answer in seconds.
Scale AI runs managed annotation workforces on long, bespoke projects, while Rapidata is self-serve and API-first: you send short tasks and get answers from a global crowd in seconds to minutes, at $4 per 1,000 responses.
Prolific recruits a standing panel paid by the hour for studies you build and host yourself, while Rapidata renders the task for you inside consumer apps and, in our 300-question head-to-head, delivered labels at 99.5%+ accuracy for $28 per 1,000 versus $228 on Prolific.
Amazon Mechanical Turk is a marketplace where you build the task and filter workers yourself, while Rapidata provides the task interface, vets every annotator up front and scores them continuously against known answers; setting up a job is as simple as pointing your agent to the Rapidata SDK and takes minutes.
By integrating in a large range of mobile apps (3000+) we have access to a large crowd of over 10 million users responding to our micro task/surveys — which we call Rapids. They are spread all over the world, so we can get human input from over 192 countries. Due to the immense size, we can parallelize tasks and get near instant results.
To get started, simply sign up for a free account on our platform. We'll start you out with $20 of credit, valid for 90 days, to play around with the tool. Check out the quickstart guide for how to set up your first order in a few minutes. If you run out of credits, get in touch and we'll find a solution.
We support images, videos, text and audio, including text-to-speech output rated by native speakers. We also support combinations — e.g., to evaluate text-image alignment, you would include the image as well as the prompt it was generated from.
You'll find the most up-to-date selection in the documentation. Our most common annotation modalities include classification (e.g. image classification or Likert rating), comparison (e.g. preference data), and select-words (e.g. highlighting misalignment in text-to-image generation). At the heart, we offer the efficient connection to the humans you need. If you don't see the modality you need, reach out at info@rapidata.ai and we can figure out a solution.
Supplying reliable, trustworthy data is crucial for us. To achieve this we have employed several measures and filtering steps, including screening questions and trust-scores, allowing us to retain only responses from verified users. We benchmarked this head-to-head against Prolific on 300 questions with known answers — see the results. Not all of these features are available in the free demo — contact us for more information at info@rapidata.ai.
Absolutely. We have a lot of experience on how to most efficiently use our platform and communicate with the annotators, which we're happy to share to ensure you get the most out of our product.
Yes — depending on the complexity of the task, we can supply responses in a matter of seconds. This is a premium feature, so please get in touch at info@rapidata.ai to set this up.
No. We only use your data for the purpose of collecting the responses presented to you. We don't sell the data, nor do we use it to train AI models. Our revenue comes from premium subscriptions required for access to additional features such as detailed targeting and larger orders.