
The Simplest Step That Makes LLMs Actually Useful
Before you reach for RLHF, before you design a reward model, before you start thinking about reinforcement learning from verifiable rewards — there’s a more fundamental question worth asking: has
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Before you reach for RLHF, before you design a reward model, before you start thinking about reinforcement learning from verifiable rewards — there’s a more fundamental question worth asking: has

A practical post. The GitHub repo LLM4Annotation (Zhen-Tan-dmml/LLM4Annotation) is a curated, updated list of papers and tools at the intersection of LLMs and data annotation. If you work in this space, it’s worth bookmarking. What’s in it: The reason it’s worth following — beyond the obvious “saves you a few
