Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents
Hugging Face Blog
Researchers extended a reinforcement-learning framework to train e-commerce conversational agents across eight realistic shopping tasks (product discovery, cart building, returns, order tracking, and others) using procedurally generated problems and algorithmically verifiable rewards. The system uses a 12-axis difficulty curriculum that scales task complexity across dimensions like constraint count, distractor products, and mid-conversation stock changes, with training conducted over 300 steps on a Qwen 3 8B model. This approach eliminates the need for human annotation or LLM judges by making all outcomes—product correctness, variant selection, hallucination detection—directly verifiable through code.
