This project originated in the Pytorch OpenEnv Hackathon and is still evolving, follow us for updates 🔥

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).

RLVE-Gym provides 400 environments for sorting, multiplication, Sudoku, and other algorithmic-reasoning tasks; however, those are all single-turn, text-in / text-out puzzles — extending to agentic domains was left as future work.

EcomRLVE-GYM fills that gap: we stay in the verifiable regime (e-commerce outcomes can be checked algorithmically) while extending to multi-turn, tool-augmented, agentic conversations — environments where the agent must act (call tools, modify world state) rather than merely reason (produce a text answer) and compensates for the deficiency of the search system.