Phase 1: Synthetic Data Generation for Self-Correction (Assigned: Timmy) #12
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Objective
Generate a massive dataset (100k+ samples) of reasoning traces focused on self-correction and error detection.
Task
Quota Target
Deep reasoning tokens for 100,000+ samples. High compute intensity.
Synthetic data generation for self-correction should specify the feedback loop: what errors are synthesized, how the synthetic examples are validated, and how they feed back into the system. The issue becomes much more actionable if tied to one measurable correction task and evaluation set.
Triaged during backlog cleanup — no comments yet. Labeling p3-low for visibility.