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Designing computational workflows for AI applications, such as chatbots and coding assistants, is complex due to the need to manage numerous heterogeneous parameters, such as prompts and ML hyper-parameters. Post-deployment errors require manual updates, adding to the challenge. The study explores optimization problems aimed at automating the design and updating of these workflows. Given their intricate nature, involving interdependent steps and semi-black-box operations, traditional optimization techniques like Bayesian Optimization and Reinforcement Learning often need to be more efficient. LLM-based optimizers have been proposed to enhance efficiency, but most still rely on scalar feedback and handle workflows with only a single component.
Microsoft Research and Stanford University researchers propose a framework called Trace to automate the design and updating of AI systems like coding assistants and robots. Trace treats the computational workflow as a graph, similar to neural networks, and optimizes heterogeneous parameters using Optimization with Trace Oracle (OPTO). Trace efficiently converts workflows into OPTO instances, allowing a general-purpose optimizer, OptoPrime, to update parameters based on execution traces and feedback iteratively. This approach enhances optimization efficiency across various domains, outperforming specialized optimizers in tasks like prompt optimization, hyper-parameter tuning, and robot controller design.
Existing frameworks like LangChain, Semantic Kernels, AutoGen, and DSPy allow for composing and optimizing computational workflows, mainly using scalar feedback and black-box search techniques. Unlike these, Trace uses execution tracing for automatic optimization, generalizing the computational graph to suit various workflows. Trace’s OPTO framework supports joint optimization of prompts, hyperparameters, and codes with rich feedback and adapts dynamically to changes in the workflow structure. It extends AutoDiff principles to non-differentiable workflows, enabling efficient self-adapting agents and general-purpose optimization across diverse applications, outperforming specialized optimizers in several tasks.
OPTO forms the basis of Trace, defining a graph-based abstraction for iterative optimization. A computational graph is a DAG where nodes represent objects and edges denote input-output relationships. In OPTO, an optimizer selects parameters, and the Trace Oracle returns trace feedback consisting of a computational graph and input on the output. This feedback can include scores, gradients, or natural language hints. The optimizer uses this feedback to update parameters iteratively. Unlike black-box setups, the execution trace provides a clear path to the output, enabling efficient parameter updates. Trace leverages OPTO to optimize various workflows by abstracting design and domain-specific components.
The LLM-based optimization algorithm OptoPrime is designed for the OPTO problem. It leverages the LLMs’ coding and debugging capabilities to handle execution trace subgraphs. Trace feedback is a pseudo-algorithm, allowing the LLM to suggest parameter updates. OptoPrime includes a memory module for tracking past parameter-feedback pairs, enhancing robustness. Experiments show OptoPrime’s efficacy in numerical optimization, traffic control, prompt optimization, and long-horizon robot control tasks. OptoPrime demonstrates superior performance compared to other optimizers, particularly when leveraging execution trace information and memory.
Trace converts computational workflow optimization problems into OPTO problems, which is demonstrated effectively with the OPTO optimizer, OptoPrime. This marks an initial step towards a new optimization paradigm with various future directions. Enhancements in LLM reasoning, such as Chain-of-Thought, Few-Shot Prompting, Tool Use, and Multi-Agent Workflows, could improve or inspire new OPTO optimizers. A hybrid workflow combining LLM and search algorithms with specialized tools could lead to a general-purpose OPTO optimizer. Specializing the propagator for specific computations, particularly large graphs, and developing optimizers capable of counterfactual reasoning could improve efficiency. Non-textual contexts and feedback could also extend Trace’s applicability.
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