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

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self-improving-agents meta-agent feedback-agent scaffold reinforcement-learning fine-tuning

Summary

This paper presents a novel blueprint for self-improving agents that combines scaffold editing and weight training through a meta-agent and feedback-agent, achieving a 14x speedup on a CUDA kernel for AlphaFold.

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.
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Cached at: 06/28/26, 08:14 PM

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.

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