Understanding the Challenges in Iterative Generative Optimization with LLMs
arXiv:2603.23994v1 Announce Type: new Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the "right" learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Differe
arXiv:2603.23994v1 Announce Type: new Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the "right" learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Different starting artifacts determine which solutions are reachable in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches do not monotonically improve generalization on BBEH. We conclude that the lack of a simple, universal way to set up learning loops across domains is a major hurdle for productionization and adoption. We provide practical guidance for making these choices.
Executive Summary
This article examines the challenges in iterative generative optimization (IGO) using large language models (LLMs). IGO is a promising approach to building self-improving agents, yet its brittleness hinders practical adoption. The authors argue that this brittleness stems from 'hidden' design choices, such as selecting the optimizer's edit scope and determining the 'right' learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, the authors demonstrate that these design choices significantly impact IGO's success, yet are often left implicit. The study concludes that the lack of a universal method for setting up learning loops across domains is a significant barrier to productionization and adoption. The authors provide practical guidance for making these design choices, highlighting the need for more explicit and domain-agnostic IGO methodologies.
Key Points
- ▸ IGO's brittleness hindering practical adoption
- ▸ Hidden design choices significantly impact IGO success
- ▸ Lack of universal method for setting up learning loops across domains
Merits
Strength
The study provides actionable guidance for making IGO design choices, offering a step towards more explicit and domain-agnostic methodologies.
Strength
The authors' use of case studies across multiple domains enhances the generalizability of their findings and provides a more comprehensive understanding of IGO's challenges.
Demerits
Limitation
The study's focus on a specific set of design choices may not capture the full range of factors influencing IGO's success, potentially limiting the scope of its findings.
Limitation
The authors' reliance on case studies may introduce biases, as the selection of domains and design choices may not be representative of the broader IGO landscape.
Expert Commentary
This article makes a significant contribution to the IGO literature by highlighting the crucial role of design choices in determining IGO success. The authors' practical guidance and emphasis on explicit design choices represent a step towards more domain-agnostic IGO methodologies. However, the study's focus on a specific set of design choices and reliance on case studies may limit its scope. Nevertheless, the article's findings have important implications for both practical adoption and policy development, underscoring the need for more explainable AI methodologies.
Recommendations
- ✓ Future research should focus on developing more generalizable IGO methodologies, incorporating a broader range of design choices and evaluating their impact across multiple domains.
- ✓ Researchers should prioritize the development of explainable AI methodologies, including IGO, to ensure transparency and trust in AI systems, particularly in high-stakes applications.
Sources
Original: arXiv - cs.LG
