Deduction-Guided Reinforcement Learning

Technique

Deduction-Guided Reinforcement Learning is a technique that combines deductive reasoning with reinforcement learning, introduced for the purpose of program synthesis. It was formalized in a 2020 publication by Chen, Wang, Bastani, Dillig, and Feng, which applied the method to synthesizing programs from specifications.

First seen 7/1/2026

Last seen 7/1/2026

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Deduction-Guided Reinforcement Learning

Overview

Deduction-Guided Reinforcement Learning is a technique that combines deductive reasoning (logical, constraint-based inference) with reinforcement learning to guide a learning-based search process. It was introduced in the context of synthesizing programs, where deduction provides structural or logical guidance to a reinforcement learning policy that navigates the space of candidate programs.

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Program Synthesis uses → 95% 1e

Deduction-Guided Reinforcement Learning is applied to program synthesis.

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CITATIONS

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[1] Deduction-Guided Reinforcement Learning was formalized in the 2020 paper 'Program Synthesis Using Deduction-Guided Reinforcement Learning' by Yanju Chen, Chenglong Wang, Osbert Bastani, Işıl Dillig, and Yu Feng. Genesys: A Novel Evolutionary Program Synthesis Tool with Continuous Optimization (reference list)

[2] The paper appears in Lecture Notes in Computer Science, pages 587–610, with DOI 10.1007/978-3-030-53291-8_30. Genesys: A Novel Evolutionary Program Synthesis Tool with Continuous Optimization (reference list)

[3] The technique's principal application domain is Program Synthesis, as indicated by the paper title and the USES relation to the Program Synthesis concept. Genesys: A Novel Evolutionary Program Synthesis Tool with Continuous Optimization (reference list)