Months ago, Stanford & OpenAI had the idea of an Intelligent Shield: A Self-Improving AI w/ scaffolding code object (controller logic, reasoning, task decomposition, code improvement, DSPy, TextGrad, CyberSec).
An old idea - already months old, in today's AI video? Absolutely, yes! Combine it with the latest code implementation from OpenAI and you will understand. Plus code implementation from Stanford's DSPy and new TextGrad.
Not yet a fully recursive, self-improvement AI? Watch my video on Gödels AI Agent for an intro. Smile.

The Self-Taught Optimizer (STOP) framework introduces a novel approach to recursive self-improvement in code generation by leveraging large language models (LLMs) like GPT-4. Instead of altering the LLM’s parameters, STOP enhances the scaffolding program—a structured process that iteratively improves itself using the LLM as a problem-solving engine. The process begins with a seed improver, which generates multiple potential solutions to a given problem, evaluates them with a utility function, and recursively refines both the solutions and the scaffolding logic over several iterations. STOP incorporates various meta-heuristics such as beam search, genetic algorithms, and simulated annealing to explore the solution space more effectively, balancing exploration and exploitation to avoid local optima. These improvements are achieved without modifying the core LLM, ensuring that the system remains computationally efficient and modular, with the ability to adapt and generalize across different tasks.

The framework’s most significant innovation lies in how it uses the LLM to recursively optimize the scaffolding program itself, not just the output solutions. This allows for dynamic adaptation of strategies during the iterative process, leading to better downstream performance, as demonstrated in tasks such as Learning Parity with Noise (LPN). Additionally, STOP’s modularity ensures that the LLM can be reused for different tasks without retraining, while the scaffolding progressively enhances the quality of the output through a combination of guided random search techniques and evaluation metrics. By integrating these methods, STOP advances the field of AI-driven optimization by illustrating how large language models can be employed for meta-level self-optimization in a controlled, recursive framework, paving the way for more sophisticated, task-specific AI agents.

Nice paper, ‪@OpenAI‬

00:00 Intro the the idea of an Intelligent Shield
06:10 Compare to old prompt engineering
06:48 OpenAI's new META-PROMPT explained
12:19 DSPy and TextGrad
14:54 The Intelligent Shield coded by the LLM
16:30 OpenAI's new SWARM code for multi-agents
19:45 Inside the Intelligent Shield (code)
30:32 Stanford on scaffolding systems w/ GPT4
32:43 Self-improvement strategies explained
40:14 What Stanford and OpenAI did not show
46:28 Current limitations
49:01 Summary


All rights w/ Authors:
For ArXiv preprint:
Self-Taught Optimizer (STOP): Recursively Self-Improving
Code Generation
https://arxiv.org/pdf/2310.02304
( I work with version 3 - published August 16, 2024)

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