Reflexion: Verbal Reinforcement Learning
Gradient-based RL needs thousands of trials and a GPU cluster to fix a failure mode. Reflexion (Shinn et al., NeurIPS 2023) does it in natural language: after each failed trial, the agent writes a reflection, stores it in episodic memory, and conditions the next trial on that memory. This is the pattern behind Letta's sleep-time compute, Claude Code's CLAUDE.md learnings, and pro-workflow's learn-rule.
Visual edition planned
This lesson isn’t interactive yet.
It is part of the curriculum and will get the same treatment as Phase 1 — a visual cover, hands-on labs, derivations with numeric checks and a quiz. Until then, the original lesson is the best place to read it:
Part of Phase 14 — Agent Engineering. Use the previous / next cards below to keep browsing the phase.