Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents

EcomRLVE-GYM is an extension of the RLVE framework, designed to enhance e-commerce conversational agents by providing eight verifiable environments that simulate real-world shopping scenarios, such as product discovery, cart building, and order tracking. These environments incorporate a 12-axis difficulty curriculum and algorithmically verifiable rewards, training agents to handle complex, multi-turn, tool-augmented tasks while optimizing for outcomes like constraint satisfaction and correct cart assembly. This approach aims to address the gap between language model fluency and practical task completion in e-commerce settings, offering an alternative to supervised fine-tuning by using reinforcement learning with verifiable rewards (RLVR). The project, which originated from the Pytorch OpenEnv Hackathon, demonstrates the potential of adaptive difficulty in training agents to perform effectively in these complex environments, emphasizing the importance of creating reward functions that are both verifiable and adaptive to the agent's capabilities.