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

StepPRM-RTL is a novel framework that enhances LLM-based RTL code generation by combining stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT). It uses dense feedback from a PRM to guide reinforcement-style updates and Monte Carlo Tree Search (MCTS) to enrich the training dataset.