StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
StepPRM-RTL is a novel framework that combines stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT) to enhance LLM-based RTL code generation. It achieves over 10% improvement in functional correctness and reasoning fidelity over prior methods on Verilog and VHDL benchmarks.
[2606.04246] StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
[Submitted on 2 Jun 2026]
Title:StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
View a PDF of the paper titled StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis, by Prashanth Vijayaraghavan and 4 other authors
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Abstract:Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL. We present StepPRM-RTL, a novel framework that combines stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT) to enhance both the functional correctness and reasoning fidelity of LLM-based RTL code generation. StepPRM-RTL constructs stepwise reasoning trajectories from canonical solutions, where each step contains a rationale and incremental code modification. A Process Reward Model (PRM) evaluates intermediate steps, providing dense feedback that guides reinforcement-style updates during RAFT fine-tuning. Monte Carlo Tree Search (MCTS) explores alternative reasoning paths, enriching the training dataset with high-quality trajectories. This integration of stepwise and outcome-aware rewards allows the model to learn both how and why to construct correct RTL, improving long-horizon reasoning beyond standard supervised or outcome-based training. Experimental evaluation on benchmark Verilog and VHDL datasets demonstrates that StepPRM-RTL outperforms the best prior methods by over 10\% in functional correctness and reasoning fidelity metrics. Ablation studies confirm that the combination of PRM-guided rewards and stepwise trajectory exploration is key to its performance. StepPRM-RTL generalizes across RTL languages and provides a scalable framework for high-fidelity, interpretable code generation, establishing a new standard for LLM-assisted hardware design automation.
Comments: 6 pages, 2 figures, DAC'2026
Subjects:
Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Computation and Language (cs.CL)
Cite as: arXiv:2606.04246 [cs.AI]
(or arXiv:2606.04246v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04246
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3770743.3804218
DOI(s) linking to related resources
Submission history
From: Prashanth Vijayaraghavan [view email] [v1] Tue, 2 Jun 2026 21:52:48 UTC (217 KB)
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