RTL-Interact
RTL-Interact is a multi-task post-training framework for training RTL-specialized large models to master the closed-loop interaction capabilities of generating RTL code, constructing EDA checks, and revising code based on tool feedback.
Timeline (last 90 days)
RTL-Interact is a multi-task post-training framework aimed at enabling a single RTL-specialized model to simultaneously master three capabilities: generating RTL implementations, constructing task-matched EDA checks, and revising code based on tool feedback.
Through automatically constructed multi-task demonstration data, together with staged supervision from chain-of-thought to tool-integrated reasoning, RTL-Interact lets the model learn when to invoke checks, how to interpret feedback, and how to make targeted modifications.
PAPO (Pattern-Aware Policy Optimization) is a multi-task reinforcement learning method built on Group Relative Policy Optimization (GRPO), used in the reinforcement learning stage of RTL-Interact.
All Facts (3)
RTL-Interact is a multi-task post-training framework aimed at enabling a single RTL-specialized model to simultaneously master three capabilities: generating RTL implementations, constructing task-matched EDA checks, and revising code based on tool feedback.
50%UnverifiedThrough automatically constructed multi-task demonstration data, together with staged supervision from chain-of-thought to tool-integrated reasoning, RTL-Interact lets the model learn when to invoke checks, how to interpret feedback, and how to make targeted modifications.
50%UnverifiedPAPO (Pattern-Aware Policy Optimization) is a multi-task reinforcement learning method built on Group Relative Policy Optimization (GRPO), used in the reinforcement learning stage of RTL-Interact.
50%