Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms
VHD-Play builds 3,300 agentic RL environments and lifts Qwen3.6-35B-A3B's mean score from 0.204 to 0.815.
VHD-Play generates agentic reinforcement-learning environments by sampling and solving a mathematical model, then rendering its decision process as stateful tools with inherited scoring. The pipeline produces 3,300 diverse environments at a few cents each. Training Qwen3.6-35B-A3B on three families raises its mean agentic score from 0.204 to 0.815, with gains on held-out instances, eight unseen mechanism families, and external function-calling, travel-planning, and 365-day e-commerce benchmarks. On E-Commerce Bench the checkpoint finishes every run without bankruptcy and exceeds Qwen3.7-Max.
- VHD-Play solves a mathematical model before rendering stateful agent tools.
- The pipeline yields 3,300 environments at a few cents each.
- Qwen3.6-35B-A3B's mean agentic score rises from 0.204 to 0.815.
- Gains transfer to held-out mechanisms, function calling, travel planning, and e-commerce.
- On E-Commerce Bench the checkpoint avoids bankruptcy and beats Qwen3.7-Max.
- Tags
- agentic-rlenvironment-generationqwenreinforcement-learningfunction-callinge-commerceworld-models
- AI models
- Qwen3.6-35B-A3BQwen3.7-Max
From the original article
1 min read at huggingface.co
Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training requires diverse agentic environments, dependable outcome signals, and low extension cost.
By Xinjie Shen, Wei Fan, Xudong Guo · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.