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EliteTable algorithm uses LLMs to improve program search for control tasks

Researchers have developed EliteTable, a novel self-improving program-search algorithm that leverages large language models (LLMs) to enhance performance on control tasks. The algorithm uses a fixed-capacity table to store high-scoring solutions, which then guides the generation of new proposals, edits, and remixes of existing solutions. Evaluations across six Gymnasium control tasks demonstrated improvements in candidate scores across all tasks and selected-winner scores in most, with notable success in CartPole-v1 where the ceiling was reached in the first generation. AI

IMPACT This research could lead to more efficient AI-driven program synthesis for control systems.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its evaluation.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

EliteTable algorithm uses LLMs to improve program search for control tasks

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The cluster contains an academic paper detailing a new algorithm and its evaluation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Stuart Farmer ·

    EliteTable: Self-Improving Program Search for Executable Control Policies

    arXiv:2610.12536v1 Announce Type: new Abstract: EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solutions to guide new proposals, edits of existing solutions, and remixes of several so…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Stuart Farmer ·

    EliteTable: Self-Improving Program Search for Executable Control Policies

    EliteTable is a self-improving program-search algorithm guided by large language models (LLMs) that uses a fixed-capacity table of high-scoring solutions to guide new proposals, edits of existing solutions, and remixes of several solutions. Performance was evaluated across six Gy…