Reinforcement learning environments that model the complete chip design loop, including tool use, design iteration, sealed grading, and evaluations.
Standard Machines’ RL environment is designed to train and evaluate models on long-horizon chip-design tasks. An episode covers choosing a microarchitecture, writing SystemVerilog, simulating, synthesizing, checking physical constraints, and revising. Tasks use executable specifications and sealed grading: submissions are checked for legality, correctness, feasibility, and then performance. The environment records latency, throughput, area, and power, and describes comparing scores with task-specific physical bounds. Standard Machines also describes private held-out evaluations and deterministic grading. The launch page says the agent’s workspace is sandboxed and has real EDA tools installed.
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