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 can teach surface-level tool use from demonstrations, but it cannot scale to the combinatorial space of constraint configurations, partial-information dialogues, and multi-step transactional workflows that real e-commerce demands.

Reinforcement learning with verifiable rewards (RLVR) offers an alternative: the agent optimises for outcomes — did the products satisfy the constraints? Was the cart correct? Was the return initiated for the right order line? The challenge is constructing reward functions that are both verifiable (no LLM-as-a-judge subjectivity) and adaptive (difficulty that grows with the policy's capability).

From RLVE-Gym to EcomRLVE-GYM

EcomRLVE-GYM transforms customer-service outcomes structurally verifiable: