Hardware-Guided Reinforcement Learning

Technique

First seen 7/2/2026

Last seen 7/2/2026

Evidence 10 chunks

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The paper introduces Hardware-Guided Reinforcement Learning (HGRL) as the core optimization technique in GenHuzz.

GenHuzz ← implements 100% 2e

GenHuzz implements the Hardware-Guided Reinforcement Learning framework for optimizing fuzzing policy.

fuzzing policy uses → 100% 2e

HGRL dynamically optimizes the fuzzing policy during hardware fuzzing.

HGRL learns inter-instruction semantics through hardware interaction.

HGRL guides the test case generation process through hardware feedback.

hardware coverage uses → 100% 2e

Hardware-Guided Reinforcement Learning uses hardware coverage as feedback to guide the fuzzer.

Reward Function uses → 100% 2e

HGRL uses a reward function that incorporates hardware coverage to optimize the fuzzing policy.

Hardware-Guided Reinforcement Learning uses Proximal Policy Optimization as its underlying RL algorithm.