RL, Reasoning, Reward Hacking, AI Timeline and Post AGI | Will Brown (Research Lead at Prime Intellect)

Reinforcement learning (RL) and agentic systems represent the critical path for evolving large language models from simple text generators into autonomous reasoning engines. Scaling reasoning capabilities—rather than merely increasing parameter counts—requires a shift toward long-horizon task execution and robust verification frameworks. Current methodologies, such as rubric engineering and verifiable tool use, remain experimental "dark arts," yet they are essential for creating self-improving loops that avoid brittle reward hacking. Decentralized compute markets are increasingly vital to democratize this research, allowing smaller teams to experiment with RL at scale. Future advancements depend on improving model calibration and internal consistency, enabling agents to assess their own difficulty and reliability. Ultimately, the transition toward reliable AI agents relies on moving beyond static instruction tuning toward dynamic, verifiable training processes that prioritize instruction following and robust, multi-step reasoning.