Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents
TL;DR — We extend the RLVE framework from single-turn reasoning puzzles to multi-turn, tool-augmented e-commerce conversations. EcomRLVE-GYM provides 8 verifiable environments — product discovery, substitution, cart building, returns, order tracking, policy QA, bundle planning, and multi-intent journeys — each with procedural problem generation, a 12-axis difficulty curriculum, and algorithmically verifiable rewards. We train a Qwen 3 8B model with DAPO over 300 steps and present early results demonstrating that environment scaling and adaptive difficulty transfer to agentic, real-world task completion.
This project originated in the Pytorch OpenEnv Hackathon and is still evolving, follow us for updates 🔥
Why RL for shopping agents?
Large language models can hold fluent conversations, yet deploying them as shopping assistants reveals a persistent gap: fluency ≠ task completion. A customer who asks “find me a USB-C charger under $25 that ships in two days” needs an agent that invokes the right catalog search, filters on three hard constraints, avoids hallucinating product IDs it never retrieved, and handles follow-ups when the top result goes out of stock.
Supervised fine-tuning