<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://datta0.github.io/</id><title>Datta's Blog</title><subtitle>Visual, math-backed explanations of LLM systems, attention, fine-tuning, GPU kernels, and the engineering details behind modern deep learning.</subtitle> <updated>2026-06-01T19:40:56+05:30</updated> <author> <name>Datta Nimmaturi</name> <uri>https://datta0.github.io/</uri> </author><link rel="self" type="application/atom+xml" href="https://datta0.github.io/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://datta0.github.io/"/> <generator uri="https://jekyllrb.com/" version="4.4.1">Jekyll</generator> <rights> © 2026 Datta Nimmaturi </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>Systems for LLM RL</title><link href="https://datta0.github.io/posts/systems-for-llm-rl/" rel="alternate" type="text/html" title="Systems for LLM RL" /><published>2026-05-30T00:00:00+05:30</published> <updated>2026-05-30T00:00:00+05:30</updated> <id>https://datta0.github.io/posts/systems-for-llm-rl/</id> <content type="text/html" src="https://datta0.github.io/posts/systems-for-llm-rl/" /> <author> <name>datta0</name> </author> <category term="LLM" /> <category term="Fine-tuning" /> <category term="RL" /> <category term="Math" /> <category term="Systems" /> <category term="GPU" /> <summary>Foray into the systems challenges and approaches for LLM RL</summary> </entry> <entry><title>Reinforcement Learning for LLMs</title><link href="https://datta0.github.io/posts/rl-for-llms/" rel="alternate" type="text/html" title="Reinforcement Learning for LLMs" /><published>2026-05-21T23:00:00+05:30</published> <updated>2026-05-31T00:58:51+05:30</updated> <id>https://datta0.github.io/posts/rl-for-llms/</id> <content type="text/html" src="https://datta0.github.io/posts/rl-for-llms/" /> <author> <name>datta0</name> </author> <category term="LLM" /> <category term="Fine-tuning" /> <category term="RL" /> <category term="GRPO" /> <category term="PPO" /> <category term="Math" /> <summary>A brief introduction to reinforcement learning for LLMs</summary> </entry> <entry><title>The MathemaTricks behind FlashAttention</title><link href="https://datta0.github.io/posts/flash-attention/" rel="alternate" type="text/html" title="The MathemaTricks behind FlashAttention" /><published>2026-04-12T14:30:00+05:30</published> <updated>2026-05-31T00:58:51+05:30</updated> <id>https://datta0.github.io/posts/flash-attention/</id> <content type="text/html" src="https://datta0.github.io/posts/flash-attention/" /> <author> <name>datta0</name> </author> <category term="Transformer" /> <category term="Attention" /> <category term="GPU" /> <category term="Kernels" /> <category term="Training" /> <category term="Fine-tuning" /> <category term="Math" /> <summary>Fast and memory efficient exact attention</summary> </entry> <entry><title>The lore behind LoRA</title><link href="https://datta0.github.io/posts/the-lore-behind-lora/" rel="alternate" type="text/html" title="The lore behind LoRA" /><published>2026-03-23T14:30:00+05:30</published> <updated>2026-05-31T00:58:51+05:30</updated> <id>https://datta0.github.io/posts/the-lore-behind-lora/</id> <content type="text/html" src="https://datta0.github.io/posts/the-lore-behind-lora/" /> <author> <name>datta0</name> </author> <category term="LoRA" /> <category term="Transformer" /> <category term="Training" /> <category term="Fine-tuning" /> <category term="Math" /> <summary>LoRA imagined from the ground up</summary> </entry> <entry><title>Exploring the Mixture of Experts</title><link href="https://datta0.github.io/posts/exploring-the-moe/" rel="alternate" type="text/html" title="Exploring the Mixture of Experts" /><published>2026-02-24T14:30:00+05:30</published> <updated>2026-05-31T00:58:51+05:30</updated> <id>https://datta0.github.io/posts/exploring-the-moe/</id> <content type="text/html" src="https://datta0.github.io/posts/exploring-the-moe/" /> <author> <name>datta0</name> </author> <category term="Mixture of Experts" /> <category term="Transformer" /> <category term="FFNN" /> <category term="Math" /> <summary>An intuitive build up to Mixture of Experts</summary> </entry> </feed>
