<!-- SC_OFF --><div class="md"><p>​</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find
<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a
<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not
<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone
<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m
<!-- SC_OFF --><div class="md"><p>After burning way too much money on GPT-4 API calls, I finally went all-in on local models with Ollama. The progress in 2026 is insane
<!-- SC_OFF --><div class="md"><p><a href="https://github.com/CrewRiz/catalyst-kv-cache">https://github.com/CrewRiz/catalyst-kv-cache</a></p> <p>I've made available a kv-cache
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