BENCHMARK-TARGETED TRAINING
Long-horizon execution in real-world professional workflows.
01 / DATASET OVERVIEW
SCOPE & PURPOSE
The training task.
- PROFESSIONAL WORKFLOWS
- ECONOMIC RELEVANCE
- VERIFIABLE OUTCOMES
Designed to evaluate agents' long-horizon task execution in real-world professional workflows, ALE provides expert-level evaluation data with economic relevance and verifiable outcomes. It supports agent training and evaluation, as well as research into cross-industry workflow deployment, long-horizon planning, and verifiable execution.
02 / DATASET PROFILE & TECHNICAL SIGNALS
- 01TASK HORIZON
- Long-horizon
- Professional workflow execution.
- 02TASK CONTEXT
- Real-world
- Economically relevant expert-level tasks.
- 03SCORING
- Deterministic
- Scorers with hidden references.
- 04PRODUCTION NETWORK
- Distributed
- Industry experts and contributors across domains.
03 / DATA PRODUCTION & CONSTRUCTION
Data production strengths
We have expert-level data production capabilities for long-horizon task scenarios. Through ongoing collaboration with the ALE team and data contribution, we have access to multi-domain task design and review resources supported by industry experts and a distributed contributor network. Our workflow covers task design, sandbox environment construction, hidden references, deterministic scorer development, and quality review.
- 01DESIGN
Multi-domain task design
Draw on industry experts and a distributed contributor network for task design and review.
- 02BUILD
Sandbox environments
Construct sandbox environments for long-horizon task scenarios.
- 03SCORE
Hidden references & scorers
Develop hidden references and deterministic scorers.
- 04REVIEW
Expert quality review
Review task quality through ongoing collaboration with the ALE team and data contribution.
04 / COVERAGE & CAPABILITY MATRIX
- 01Workflow scope
- Real-world professional workflowsCross-industry deployment
- 02Execution demands
- Long-horizon planningVerifiable execution
- 03Production resources
- Industry expertsDistributed contributorsMulti-domain review
- 04Evaluation tools
- Sandbox environmentsHidden referencesDeterministic scorers