From RL’s Early Days to Its Future: A Four‑Stage Evolution of Reinforcement Learning

This reflective essay traces reinforcement learning’s decade‑long evolution through four stages—early algorithmic foundations, application‑driven growth, problem‑construction focus, and speculative future—while critiquing the expanding definition and its impact on research and industry.

AI researchRL evolutionRLHF

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From RL’s Early Days to Its Future: A Four‑Stage Evolution of Reinforcement Learning