LLM Evolution: From RLHF to Chain-of-Thought Reasoning
ZeroStateReflex · x · 2026-08-22
This post summarizes the key milestones in the rapid evolution of LLMs over the past few years:
- 2018-2020: The belief that models get smarter simply by being very large.
- 2020-2022: The realization (led by OpenAI) that models don't necessarily need to be bigger, but require vast amounts of data—essentially the entire internet and every book.
- 2022: The advent of RLHF (Reinforcement Learning from Human Feedback), which turned base models into conversational assistants.
- 2024: The introduction of "chain-of-thought" (giving models "pen and paper"), leading to reasoning models capable of working on hard problems for minutes or hours.
- 2024-2025: A shift in training focus to RLVR (Reinforcement Learning from Verification Rewards).
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