Edit-Trace Oversight: Scalable Alignment Signal from Agentic Workflows
Edit-traces from production agentic workflows produce alignment signal that is denser, more outcome-predictive, and distributionally unlike conventional RLHF preference data. 80.7% of edits are substantive rewrites; binary rejection correlates with 78% positive outcomes — the strongest oversight signal.
Edit-Trace Oversight: Scalable Alignment Signal from Agentic Workflows
Volodymyr Ovcharov — LEX AI LLC, Kyiv, Ukraine
Abstract
Edit-traces from production agentic workflows produce alignment signal that is denser, more outcome-predictive, and distributionally unlike conventional RLHF preference data. Three experiments on a single-practitioner case study (30,510 edit pairs, 2,892 sessions, 1,579 attributed outcomes): (1) 80.7% of edits are substantive rewrites; (2) process-level behavioral features are significant but redundant with artifact features; (3) binary rejection correlates with 78% positive outcomes — the strongest oversight signal.