@zostaff: This paper completely changed how I think about self-improving agents: Initialize -> Run -> Analyze -> Branch -> Update…

This paper completely changed how I think about self-improving agents: Initialize -> Run -> Analyze -> Branch -> Update Here is the 5-step blueprint: Initialize: A Meta-Agent builds the agent's first scaffold from a task spec and a verifier, that's all it needs. Run: The agent executes in a sandbox and the full trajectory is logged, every prompt, tool call and response, not one summary metric. Analyze: A Feedback-Agent reads that trajectory and diagnoses specific failure modes instead of reacting to statistics. Branch: At each step the Feedback-Agent itself picks a lever, fix the scaffold (prompts, tools, retries) or train the weights via RL. Update: Even the RL method is chosen per task, GRPO, PPO, DPO, entropic weighting, based on the shape of the reward. The key insight: The scaffold changes how the agent searches, the weights change what the model knows, one lever never saturates the other. On a CUDA kernel for AlphaFold, a scaffold edit gave a 1.14x speedup, but training weights on top cut runtime by 91.9% for a final 14x. Read this, then check the article below.