RLTL;DR is a new reinforcement learning method that enables agent self-improvement without successful examples. The approach conditions subsequent rollouts on self-generated insights produced after each failed attempt, bypassing the need for successful training trajectories. This technique provides a scalable way for language models to learn from failure when solving extremely difficult tasks.
Opening Kapyn…