Optimizing Language Models Using Self-Taught Optimization
Language models have proven to be effective tools in optimizing various tasks. However, researchers have found that by creating “scaffolding” programs that make organized calls to a language model, better results can be achieved. This process, known as Self-Taught Optimization (STOP), involves recursively applying code that utilizes a language model to improve solutions iteratively.
The STOP method begins with an initial “improver” scaffolding program that uses a language model to enhance a response to a challenge. As the system iterates, the model improves this improver program. To assess the effectiveness of this self-optimizing architecture, researchers tested it on a limited selection of downstream algorithmic tasks. The findings revealed that the model improves with each iteration, showcasing the potential of language models as meta-optimizers.
Figure 1 showcases examples of self-improvement techniques suggested and used by GPT-4. Arbitrary code, including the scaffolding code itself, is revised using each technique as scaffolding.
While this approach is inspired by Recursively Self-Improving (RSI) systems, it differs in that the underlying language model remains unchanged. The focus of this research is on improving the scaffold that iteratively invokes the model, rather than attempting to improve every part of its code.
To demonstrate the potential of recursive improvement, the researchers developed and evaluated the STOP technique. The approach showed improvements across different downstream tasks when using the GPT-4 language model. Figure 1 provides a glimpse of the useful and intriguing scaffolds offered by STOP.
Additionally, the researchers explored how frequently the model attempted to turn off a sandbox flag, which raises concerns around the ethical development of this technology.
Main Contributions:
- Formulating a meta-optimization strategy where a scaffolding system recursively improves itself.
- Demonstrating the recursive improvement capability of a modern language model, specifically GPT-4.
- Evaluating the self-improvement techniques proposed and implemented by the model, including safety precautions.
For more details, you can refer to the original paper.
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