Hardware-Guided Reinforcement Learning

Technique

Hardware-Guided Reinforcement Learning is a fuzzing technique introduced within the GenHuzz white-box hardware fuzzing framework, in which a reinforcement learning loop uses real-time feedback from the hardware under test (DUT) to optimize the token-level generation policy of a language-model-based fuzzer producing RISC-V assembly test cases.

First seen 7/2/2026

Last seen 9/1/2026

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Hardware-Guided Reinforcement Learning

Overview

Hardware-Guided Reinforcement Learning (HGRL) is a reinforcement-learning technique that drives the generation of hardware test cases by treating the device under test (DUT) itself as the reward signal. In the GenHuzz white-box hardware fuzzing framework, HGRL dynamically optimizes the token-by-token policy of a language-model-based fuzzer that emits RISC-V assembly instructions, with the objective of maximizing hardware coverage and exposing deeper hardware bugs and vulnerabilities. The technique is designed to learn and exploit complex inter-instruction semantics that static training corpora cannot capture.

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NEIGHBORHOOD

9 nodes · 16 edges

graph · Hardware-Guided Reinforcement Learning · depth=1

RELATIONSHIPS

12 connections

GenHuzz ← implements 100% 4e

GenHuzz implements the Hardware-Guided Reinforcement Learning framework to dynamically optimize its fuzzing policy based on hardware feedback.

Reward Function uses → 100% 4e

HGRL uses a reward function to assign feedback to the fuzzer based on instruction validity and coverage.

fuzzing policy uses → 100% 4e

HGRL dynamically adjusts the fuzzing policy based on hardware feedback.

hardware coverage uses → 100% 3e

The Hardware-Guided Reinforcement Learning framework incorporates real-time feedback from the hardware to optimize coverage.

reset module for exploration-exploitation balance uses → 100% 2e

HGRL includes a reset module to balance exploration and exploitation.

Proximal Policy Optimization uses → 100% 2e

The HGRL framework uses PPO to optimize the fuzzing policy.

Instruction Semantics uses → 95% 2e

HGRL learns inter-instruction semantics through hardware interaction.

Testcase Generation uses → 95% 2e

HGRL guides the test case generation process through hardware feedback.

GenHuzz: An Efficient Generative Hardware Fuzzer ← introduces 100% 2e

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

device under test depends on → 90% 2e

HGRL depends on the DUT as the environment providing coverage feedback.

GenHuzz ← uses 100% 1e

GenHuzz dynamically optimizes instructions through a Hardware-Guided Reinforcement Learning framework.

hardware coverage feedback uses → 100% 1e

HGRL uses hardware coverage feedback to refine the fuzzer and scorer iteratively.

LINKED ENTITIES

2 links

GenHuzz USES Extracted graph relationship

hardware coverage USES Extracted graph relationship

CITATIONS

8 sources

8 citations — click to expand

[1] Hardware-Guided Reinforcement Learning is a reinforcement-learning framework that incorporates real-time feedback from the hardware to optimize the fuzzing policy in GenHuzz. GenHuzz: An Efficient Generative Hardware Fuzzer

[2] GenHuzz reframes fuzzing as an optimization problem, uses a language model to generate RISC-V assembly instructions, and dynamically optimizes them through Hardware-Guided Reinforcement Learning with real-time hardware feedback to understand complex inter-instruction interdependences. GenHuzz: An Efficient Generative Hardware Fuzzer

[3] The fuzzer begins with N assembly instructions concatenated into a sequence D using a SEP separator, and each instruction I_i is tokenized into T_i = (t_{i,1}, ..., t_{i,k_i}). GenHuzz: An Efficient Generative Hardware Fuzzer

[4] Token-level reward assignment is non-trivial because rewards are delayed until a complete instruction is formed, branch instructions can skip subsequent instructions, and syntactically correct instructions may still cause DUT exceptions. GenHuzz: An Efficient Generative Hardware Fuzzer

[5] GenHuzz classifies each instruction's status on the DUT as valid & executed, valid & failed, valid & unexecuted, or invalid & unexecuted, and uses this classification to assign rewards to token-level actions. GenHuzz: An Efficient Generative Hardware Fuzzer

[6] Algorithm 1 (State Transition and Identification) implements the HGRL loop: at each step the fuzzer takes an action from the current observation, and the environment advances via step(env, action) until done. GenHuzz: An Efficient Generative Hardware Fuzzer

[7] Because the RISC-V Golden Reference Model used does not support speculative or out-of-order processing, GenHuzz (and its HGRL component) can only detect static bugs. GenHuzz: An Efficient Generative Hardware Fuzzer

[8] Evaluation on three RISC-V cores shows GenHuzz achieves significantly higher hardware coverage with fewer test cases than four state-of-the-art fuzzers, detects all known bugs reported in prior studies with fewer test cases, and uncovers 10 new vulnerabilities (5 of which are the most severe hardware vulnerabilities ever detected by a hardware fuzzer targeting the same cores, with CVSS v3 severity scores exceeding 7.3). GenHuzz: An Efficient Generative Hardware Fuzzer