Process Supervision & Chain-of-Thought Data
Step-by-step reasoning verification datasets evaluating intermediate logic steps for complex math, coding, and multi-step AI reasoning.
Hardware & Sensor Specifications
DOMAIN EXPERTS
PhD Mathematicians & Senior Software Architects
VERIFICATION SUITE
Automated Python Sandbox + Unit Test Execution
STEP GRANULARITY
Sub-sentence step-by-step logic breakdown
FORMAT
Markdown Chain-of-Thought with Step Reward Tokens
Target Tasks & Execution Scenarios
- • Mathematical Proof Step Verification
- • Code Logic & Test Suite Verification
- • Multi-Step Financial Reasoning
- • Legal Document Inference Audit
Individual Types in Process Supervision & Chain-of-Thought Data (3)
Click any specific Type card below to open its dedicated page with complete 12-section technical details:
[SPECIFIC TYPE]
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Step-by-Step CoT Verification
Labeling correctness of individual logic steps in complex reasoning chains.
[SPECIFIC TYPE]
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Code Execution Unit Test Benchmarks
Automated code unit testing paired with human developer logic review.
[SPECIFIC TYPE]
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Error Identification & Patching Datasets
Pinpointing exact reasoning step failures and generating corrected logic steps.
Quality Standards (Right vs Wrong)
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Evaluating only final answer | PASS: Validating every intermediate logic step |
| FAIL: Ignoring subtle math fallacies | PASS: Pinpointing exact step-level reasoning errors |