Combining Large Language Models and Gradient-Free Optimization for Automatic Control Policy Synthesis

• URL: http://arxiv.org/abs/2510.00373v1
• Date: Wed, 01 Oct 2025 00:42:15 GMT
• Title: Combining Large Language Models and Gradient-Free Optimization for Automatic Control Policy Synthesis
• Authors: Carlo Bosio, Matteo Guarrera, Alberto Sangiovanni-Vincentelli, Mark W. Mueller,
• Abstract summary: Large Language models (LLMs) have shown promise as generators of symbolic control policies.<n>We propose a hybrid approach that decouples structural synthesis from parameter optimization.<n>We show that combining symbolic program synthesis with numerical optimization yields interpretable yet high-performing policies.
• Score: 2.8593976574111264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language models (LLMs) have shown promise as generators of symbolic control policies, producing interpretable program-like representations through iterative search. However, these models are not capable of separating the functional structure of a policy from the numerical values it is parametrized by, thus making the search process slow and inefficient. We propose a hybrid approach that decouples structural synthesis from parameter optimization by introducing an additional optimization layer for local parameter search. In our method, the numerical parameters of LLM-generated programs are extracted and optimized numerically to maximize task performance. With this integration, an LLM iterates over the functional structure of programs, while a separate optimization loop is used to find a locally optimal set of parameters accompanying candidate programs. We evaluate our method on a set of control tasks, showing that it achieves higher returns and improved sample efficiency compared to purely LLM-guided search. We show that combining symbolic program synthesis with numerical optimization yields interpretable yet high-performing policies, bridging the gap between language-model-guided design and classical control tuning. Our code is available at https://sites.google.com/berkeley.edu/colmo.

Related papers

• OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization [21.882017397032964]
  We present OptiML, an end-to-end framework that maps either natural-language intent or input code to performance-optimized kernels.<n>A search-based (OptiML-X) then refines either synthesized or user-provided kernels using Monte Carlo Tree Search over LLM-aware, guided by a hardware-driven reward derived from profiler feedback.
  arXiv  Detail & Related papers  (2026-02-12T04:50:19Z)
• Policy-Conditioned Policies for Multi-Agent Task Solving [53.67744322553693]
  In this work, we propose a paradigm shift that bridges the gap by representing policies as human-interpretable source code.<n>We reformulate the learning problem by utilizing Large Language Models (LLMs) as approximate interpreters.<n>We formalize this process as textitProgrammatic Iterated Best Response (PIBR), an algorithm where the policy code is optimized by textual gradients.
  arXiv  Detail & Related papers  (2025-12-24T07:42:10Z)
• LOOPRAG: Enhancing Loop Transformation Optimization with Retrieval-Augmented Large Language Models [23.6344001089164]
  LOOPRAG is a retrieval-augmented generation framework designed to guide Large Language Models (LLMs) in performing effective loop optimization.<n>We introduce a parameter-driven method to harness loop properties, which trigger various loop transformations, and generate diverse yet legal example codes.<n>To enhance correct and efficient code generation, we introduce a feedback-based iterative mechanism that incorporates compilation, testing and performance results.
  arXiv  Detail & Related papers  (2025-12-12T11:09:48Z)
• SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations [25.18297372152296]
  SOCRATES is a novel two-stage procedure that automates the design of tailored SO algorithms.<n>An ensemble of digital replicas of the real system is used as a testbed to evaluate a set of baseline SO algorithms.<n>An LLM acts as a meta-optimizer, analyzing the performance trajectories of these algorithms to iteratively revise and compose a final, hybrid optimization schedule.
  arXiv  Detail & Related papers  (2025-11-01T19:57:38Z)
• Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization [20.44067161623662]
  Large language models (LLMs) have demonstrated remarkable capabilities in code generation and structured reasoning.<n>We propose a novel neural-symbolic framework that formulates prompt selection as a sequential decision process guided by Monte Carlo Tree Search.<n>Our method explores and refines multi-step prompt sequences for the goal of improving code generation quality.
  arXiv  Detail & Related papers  (2025-08-08T04:01:24Z)
• Compiler Optimization via LLM Reasoning for Efficient Model Serving [7.257845254223727]
  We introduce a compilation framework (dubbed REASONING COMPILER) that formulates optimization as a sequential, context-aware decision process.<n>We achieve substantial speedups with markedly fewer samples than leading neural compilers.
  arXiv  Detail & Related papers  (2025-06-02T07:02:46Z)
• Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? [45.58422897857411]
  This work explores the use of large language models (LLMs) for hyperparameter optimization by fine-tuning a parameter-efficient version of Code Llama using LoRA.<n>Our approach achieves competitive or superior Root Mean Square Error (RMSE) while substantially reducing computational overhead.<n>Results demonstrate that LLM-based optimization not only rivals established Bayesian methods like Tree-structured Parzen Estimators (TPE) but also accelerates tuning for real-world applications requiring perceptual quality and low-latency processing.
  arXiv  Detail & Related papers  (2025-04-08T13:15:47Z)
• Training of Scaffolded Language Models with Language Supervision: A Survey [62.59629932720519]
  This survey organizes the literature on the design and optimization of emerging structures around post-trained LMs.<n>We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling [62.19438812624467]
  Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning.<n>We propose OptiBench, a benchmark for End-to-end optimization problem-solving with human-readable inputs and outputs.
  arXiv  Detail & Related papers  (2024-07-13T13:27:57Z)
• LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning [69.95292905263393]
  We show that gradient-based and high-level LLMs can effectively collaborate a combined optimization framework.<n>In this paper, we show that these complementary to each other and can effectively collaborate a combined optimization framework.
  arXiv  Detail & Related papers  (2024-05-30T06:24:14Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts.<n>We identify two pivotal factors in model parameter learning: update direction and update method.<n>We develop a capable Gradient-inspired Prompt-based GPO.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
  We present the first efficient scalable and general framework that can directly search on the tasks of interest. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
  arXiv  Detail & Related papers  (2022-09-27T17:51:31Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.