StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

2026-06-02

AI Summary

This work addresses the challenges of automatic RTL code generation—particularly long-horizon reasoning, multi-step dependencies, and stringent functional correctness—by introducing a reinforcement learning framework that integrates step-by-step reasoning trajectory modeling, a process reward model (PRM), and retrieval-augmented fine-tuning (RAFT). The approach further incorporates Monte Carlo tree search (MCTS) to explore high-quality reasoning paths. Notably, it is the first to jointly optimize process rewards and trajectory exploration, enabling large language models to simultaneously learn both “how to reason” and “why to reason” in a given way. This synergistic optimization substantially enhances long-horizon reasoning capabilities. Empirical evaluations on Verilog and VHDL benchmarks demonstrate consistent improvements over state-of-the-art methods, with gains exceeding 10% in both functional correctness and reasoning fidelity.

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.

Problem

• RTL synthesis
• automatic code generation
• long-horizon reasoning
• functional correctness
• hardware design automation

Innovation

• Process Reward Model
• Stepwise Reasoning
• Retrieval-Augmented Fine-Tuning
• Monte Carlo Tree Search
• RTL Synthesis