How LLMs Learned to Reason: SFT --> RLHF --> RLVR

<h1> How LLMs Work: A Journey Through Tokens, Attention, and Transformers </h1> <p>“My favourite rock band is…”</p> <p>How does a large language model take that unfinished

<p>57% of organizations estimate their data isn't AI-ready. General-purpose LLMs handle broad tasks well but hallucinate on specialized queries, miss domain jargon, and can't

<p>The famous InstructGPT result is still the cleanest argument for post-training: a 1.3B aligned model was preferred over the 175B GPT-3 base ~85% of the time on

<p>Most "multimodal" AI products today are really a relay team. One model plans, then hands the job to an image model, a video model, a detector, or a robot policy. Reka's new

<h1> Cognitive Architectures of AGI: 7 Patterns That Transform LLMs from Oracles into Thinkers </h1> <p><em>Why does ChatGPT sometimes deliver brilliant insights and other times

<!-- SC_OFF --><div class="md"><p>Every time one of our agents made a bad call in production, we had the same problem. Logs showed what happened. Nothing showed why it decided to

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1ckkm/why_llms_sound_right_but_fail_to_actually_do/"> <img alt="Why LLMs sound right but fail to actually