StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
Quick Take
StepPRM-RTL is a novel framework that enhances RTL code generation by over 10% in functional correctness and reasoning fidelity through stepwise trajectory modeling and process-reward modeling. It integrates Monte Carlo Tree Search for alternative reasoning paths, establishing a new standard for LLM-assisted hardware design automation across RTL languages.
Key Points
- StepPRM-RTL improves RTL code generation by over 10% in key metrics.
- Combines stepwise reasoning and process-reward modeling for enhanced performance.
- Utilizes Monte Carlo Tree Search to enrich training datasets with high-quality trajectories.
- Generalizes across RTL languages, providing a scalable framework for code generation.
- Ablation studies confirm the importance of PRM-guided rewards in its success.
Article Content
From source RSS / original summaryarXiv:2606. 04246v1 Announce Type: new 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.
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