Patrick Nercessian
Project OwnerLead the delegation of tasks and oversight of progress on each milestone. Utilize prior experience with evolutionary methods to guide and inform on structural decisions of the evolution.
This proposal seeks to promote the application of reinforcement learning principles within the MOSES (Meta-Optimizing Semantic Evolutionary Search) algorithm through the strategic integration of Large Language Models (LLMs). The design is conceptually inspired by Q-learning, and our approach would be to experiment with using LLMs in place of Q-learning functions to estimate the utility of specific evolutionary directions in MOSES. By integrating LLMs into the generational modeling and fitness estimation stages of the evolutionary search, we believe there is potential to guide evolutionary operations to efficiently explore and exploit the search space for optimal solutions.
This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.
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Develop the building blocks for the LLM integration with MOSES.
A preliminary framework for LLMs to perform utility evaluation of variation operators. Design and integration of the data structure for input to the LLM (current evolutionary states and actions). Simulated initial utility estimate outputs for these actions.
$20,000 USD
We will have succeeded here if we have a framework which can take in a data structure representing the state of an individual and possible action(s) and output an esimated utility for the action(s).
A more cohesive framework which will allow relatively easy setup (for a new problem domain) to run an evolutionary algorithm using LLMs to direct favorable variation operators.
An implemented modular architecture for integrating utility estimation. An implemented state-action representation for generational modeling & fitness estimation. An LLM layer for fitness estimation (directed mutation/crossover) Integration of above deliverables into MOSES, and simulation of initial search process.
$15,000 USD
We will have succeeded here if a typical software engineer could use our framework to run MOSES evolutionary algorithms with LLM-directed variation for a new problem domain.
Run initial experiments, and utilize the results to make decisions on chosen LLMs, prompts, and other hyper-parameters or design choices. Iterate on the framework repeatedly and improve its performance based on these findings.
Evaluate prospective LLMs via empirical testing on utility estimation tasks (or related tasks) and select a production set of LLMs based on their performance. Integrate the selected LLMs with MOSES and continuously tune the prompts for each model. Run initial evolutionary experiments to assess the impact of LLM-informed decisions.
$12,500 USD
We will have succeeded here by getting initial results from our new framework, having made decisions on tweaks to the framework based on preliminary results, and have repeated these steps multiple times.
Consolidate results of our experiments by comparing results based on: A) differences between the runs B) similar runs executed via standalone MOSES usage
Analyze the data from experiments to identify successful strategies and common patterns. Compare the performance of different LLM-enhanced MOSES configurations (e.g., Moses-GPT-4, Moses-Claude-Sonnet-3.5, Moses-Llama3-70B) against traditional MOSES implementations. Compile documentation regarding implementation and experimental methods. Prepare findings for publication and further research. Document and release the enhanced MOSES framework with comprehensive instructions.
$12,500 USD
We will have succeeded here by having a final report documenting our journey and findings as it relates to framework-internal decisions and to baseline MOSES attempts.
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