From RLHF to RL Environments - Welcoming Agents to the Real World

LLMs have reached a glass ceiling. Models continue to improve, but static data and common benchmarks cannot teach systems to operate in chaotic and multi-objective settings.

Progress now depends less on scale and more on the environments in which models learn. At Invisible, we believe reinforcement learning environments and evolving evaluations are the core ingredients of this shift. By transitioning from RLHF to verifiable reward systems and reinforcement learning with AI feedback, agents train inside conditions that resemble the real world. They learn to make tradeoffs, use tools, reason through uncertainty, and adjust based on consequences rather than simple instructions.

This session outlines why static training has stalled, how reward-driven RL environments support deeper reasoning, and why evaluations must become multi-objective, verifiable, and informed by human expertise to capture real world skills such as judgment, tone, and strategy.

Join us to examine how teaching AI to reason within realistic environments opens new research territory and why the next breakthroughs will come from smarter environments.