Proving Agent Learning via Rollback: Is It Luck or Skill?
go_kul_07 · reddit · 2026-09-01
How to distinguish between genuine learning and luck when building self-improving agents? Borrowing from scientific methods, the author proposes storing the inverse of every memory lesson, enabling a rollback experiment:
- Baseline (no lessons): 46.3% success
- Lessons applied: 68.7%
- Lessons rolled back: 47.0% (returns to baseline)
- Lessons re-applied: 67.0%
Run with a frozen model and temperature 0, transitions are significant at p < 0.0001, costing only $0.70. This highlights a flaw in many agent memory systems: in-place edits make rollback impossible, conflating learning with drift. The author is integrating this into Areev, an open-source engine for governed agent memory where rollback is a first-class operation.
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