Technology Radar
Agentic reinforcement learning environments provide a training ground for LLM-based agents, combining the context, tools and feedback to complete multi-step tasks. This approach reframes post-training of LLMs from simple single-turn outputs to agentic behaviors such as reasoning and tool use, with rewards or penalties assigned to each action. Techniques such as RLVR help ensure these rewards are verifiable and resistant to gaming.
AI research labs are currently driving the development of these environments, particularly for coding and computer-use agents. One example outside of the frontier labs is Cursor's Composer, a specialized coding model trained within their product environment. Organizations building agentic systems should consider whether creating reinforcement learning environments could help train more capable and domain-specific models.
Setting up the required infrastructure can be complex. However, frameworks and platforms are emerging to simplify the process, including Prime Intellect's environments hub, Agent Lightning and NVIDIA NeMo Gym. We recommend exploring this approach where it can deliver more capable and cost-effective models for your domain.
