Self-improving agents learn from their own modification history

The key insight is that SelfSearch treats an agent's own failed attempts as training data rather than waste. This inverts the typical loop where you run an agent, measure it against a task, and backprop the loss. Here, the agent learns from introspection on what it tried and why it didn't work.

This connects directly to the broader shift toward modular, auditable agent architectures we've been tracking. The Auditable Long-Term Memory piece from late September showed how decoupling memory from inference lets you measure and swap components independently. SelfSearch applies the same principle to the optimization loop itself, removing the need for external task evaluation as the bottleneck. Similarly, Dr. OPD's work on selective weighting of training signals suggests that not all supervision is equal; SelfSearch extends that insight by asking whether external supervision is necessary at all for certain improvement cycles. The difference is scope: Dr. OPD optimizes knowledge transfer between models, while SelfSearch optimizes an agent's self-directed iteration.

If SelfSearch agents trained without external reward signals match or exceed the performance of reward-trained baselines on a held-out task suite within six months, that confirms the approach scales beyond toy problems. If the same team or competitors report wall-clock speedups in agent iteration cycles (not just theoretical cost reduction), the practical adoption curve begins.