RSIAgent's memory loop

What's this about?

RSIAgent coordinates a curriculum, actor, verifier and persistent task memory while holding the underlying model fixed. The authors report gains on computer-use and exam tasks; the comparisons need independent reproduction.

What exactly is training-free self-improvement?

The model weights stay the same, but the system learns from prior attempts through better tasks, feedback and stored experience. That differs from an AI rewriting and retraining itself without limit.

What's the community saying?

1. Memory and verification can lift agent performance without new weights.
2. Gains might depend on benchmark-specific experience.
3. The term “RSI” can overstate what a bounded harness actually does.