DATA - FOR REINFORCEMENT LEARNING
The Training Data Agents Actually Need
Expert-curated agentic RL data and evaluation datasets targeting the dimensions that move agents from benchmark performance to real-world performance.
The training signal frontier models are missing comes from real experts doing real work.
Reinforcement learning has produced superhuman performance in math and coding where there are verifiable rewards. But the next frontier is different. Law. Finance. Marketing. Operations. These domains are open-ended with nonverifiable rewards. What matters is observing real experts operating inside real environments, and preserving the reasoning process that leads to their decisions. That's the training signal frontier models need.
Highlights
The Approach
Curriculum design, not annotation.
Make a portfolio dashboard for the recent …
Make
Define the tasks
Define the target
What should the agent be able to do in the real world?
Environment
Context
Tasks
Participants
Tools Available
Design the environment
What task, tools, and context would require that ability?
# checkpoint 1
# checkpoint 2
Chats
Website wireframe ideas
Asset ideas for Vanta landing page
Collect expert trajectories
How do skilled humans actually solve this?
Rubric 1
Rubric 2
Rubric 3
Can this candidate break a
vague need into
well-scoped sub-tasks an
AI can act on individually?
Pass
Create me a
“Vanta horse logo”
with using
ChatGPT
that I can use it
on my website brand
Start
Produce reward signals
What does good performance look like, assessed by expert rubric?
BREAKDOWN
High-quality agentic RL data confusing on economically valuable, open-ended, and non-verifiable rewards tasks.
For RL - Agentic Training Data
Overview
Real applications, rebuilt for RL — Simulated APIs, MCP servers, and GUIs mirroring production systems.
Expert-curated artifacts
Hard tasks with defined learning objectives
Easy integration
For Eval - Benchmarking Datasets
Overview
Curated benchmarks measuring what standard benchmarks miss.
Domain-specific
Non-verifiable dimensions
Ability-mapped
Regularly refreshed
Quality Engine
Overview
Every dataset starts with a defined target.
Vetted, trained, calibrated data with experts.
Real AI-native workflows, grounded in what the agent needs to learn.
Consistency, format validation, reward signal verification.
LEARN MORE
Talk To A Human
Emergences Labs offer high-quality agentic RL data that improves the performance of our agent by 20%.
D. K.
Founder at Stealth Startup
