Continual Learning for AI Agents: From Failures to Durable Improvements - Soheil Feizi, RELAI
TL;DR
Agent learning isn't just model fine-tuning: Useful updates often happen in the harness (prompts, tools, code) and memory layers, which are cheaper and faster than retraining weights.
Production logs aren't learning environments: You need to transform logs into replayable simulations with mock tools, synthetic users, and defined evaluators to make learning testable.
The regression problem is real: A new fix can silently break what previously worked, creating hidden regressions that go undetected without proper testing.
Four principles govern practical VCL: Replayability, holisticness, lifelongness, and efficiency must all be addressed for durable agent improvements.
Rely's tool adds VCL in two commands: Create learning environments from signals, then optimize with regression-aware updates that compound over time.
Summary
Fixing an agent's latest failure often breaks what previously worked. Soheil Feizi introduces verifiable continual learning, a framework where every improvement is tested against past environments to ensure fixes help without creating regressions.
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