Self-Improvement via Internalized Self-Generated Feedback

This post introduces RLTL;DR, a novel self-improvement paradigm for reinforcement learning agents facing extremely difficult tasks with no external guidance. It enables agents to generate their own 'TL;DR' insights after failed attempts, condition subsequent rollouts on these insights, and internalize a direct task-to-insight mapping through backpropagation. This approach significantly breaks through learning barriers where standard methods fail, achieving notable success rates even when insights are not present at evaluation time. The core contribution is the demonstration that learning from self-generated, internalized feedback is a powerful and efficient training paradigm.