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VHD-Play generates 3,300 agent environments from solved mechanisms

huggingface.co Editor score 8/10

VHD-Play derives executable dynamics and trajectory scoring from the same solved model, generating 3,300 environments for a few cents each; Qwen3.6-35B-A3B training raises mean agentic score from 0.204 to 0.815. The approach offers a way to scale agent training while keeping task mechanics and evaluation aligned.

What

VHD-Play generates agentic reinforcement-learning environments by solving mathematical models before rendering their decision processes as stateful tools.

Method

The same solved model supplies executable dynamics and a trajectory-scoring reference, keeping environment behavior and evaluation aligned.

Why it matters

The pipeline produces 3,300 environments and training gains that extend to held-out mechanisms and external benchmarks, supporting scalable agent training.

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