Training Language Models to Follow Instructions (InstructGPT)
Training Language Models to Follow Instructions (InstructGPT) is one of 15 landmark papers in TheLLMWiki's research index — explained here in plain language, without the jargon.
What this paper showed
Introduced reinforcement learning from human feedback (RLHF) as a way to align a base language model with what people actually want from it, rather than just what it was trained to predict. This technique became the core method behind ChatGPT's original 2022 release.
For the full technical detail — architecture diagrams, training data, ablations and exact benchmark numbers — the original paper is the authoritative source; this page exists to give you the plain-language version before you decide whether to read further.
The lasting impact of Training Language Models to Follow Instructions (InstructGPT)
RLHF (and its descendants like DPO) remain the standard way labs turn a raw base model into something that follows instructions safely and helpfully. Nearly every consumer-facing model you can name went through some version of this process.
Papers earn a place in this index specifically because their core idea is still visible in production systems today, not just because they were influential when published. If you're trying to understand why a current model or technique works the way it does, tracing it back to a paper like this one is usually more useful than reading a summary of the model's release notes alone.
Training Language Models to Follow Instructions (InstructGPT), answered
Who published Training Language Models to Follow Instructions (InstructGPT)?
This paper came out of OpenAI.
Why does this paper matter today?
RLHF (and its descendants like DPO) remain the standard way labs turn a raw base model into something that follows instructions safely and helpfully. Nearly every consumer-facing model you can name went through some version of this process.
Where can I read the full paper?
Search the paper title on arXiv or Google Scholar for the original PDF and any follow-up work that has cited it.
Do I need to read the full paper to understand the idea?
Not necessarily — the plain-language summary above covers the core contribution. The full paper matters most if you're implementing the technique yourself or need the exact experimental details.
What should I read next?
See the related papers above, or the AI research hub for more landmark work organized by topic.
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