maths
maths at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Streamline AI Projects: Balancing Research, Work, and Personal Pursuits”, “Explore how LARA makes frozen LLMs adaptable with lightweight modular behaviors”, and “Demystifying the math behind the algorithms powering modern LLMs”. Balancing research, a new corporate role, and personal projects is a lot, and the anxiety that comes with it is completely understandable. Most post-training work assumes you must bolt new weights onto a model. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every maths story on Beyond Market Intelligence, newest first.
Streamline AI Projects: Balancing Research, Work, and Personal Pursuits
Balancing research, a new corporate role, and personal projects is a lot, and the anxiety that comes with it is completely understandable. You are not alone in feeling that the advice out there is jumbled. Instead of chasing every new model, start by mapping each tool to a specific stage in your workflow. For deep ideation, your current GPT subscription is a solid anchor. For coding and architecture, Claude's models often excel.
Explore how LARA makes frozen LLMs adaptable with lightweight modular behaviors
Most post-training work assumes you must bolt new weights onto a model. LARA challenges that by training small, low-rank residual adapters at selected layers, leaving the frozen LLM untouched. The result is a library where behaviors like coding, maths, or medical reasoning are separate, lightweight files. You can load them, blend them, or route them per token. The Mixture of Behaviors demo shows a single model switching between Hemingway and Fitzgerald styles. It is practical, modular research.
Demystifying the math behind the algorithms powering modern LLMs
If you've been following the technical reports from Kimi, DeepSeek, Qwen, and GLM, you've likely noticed how much on-policy distillation and GRPO-style algorithms now power the frontier. That's why this deep dive is so timely. It unpacks the maths and code behind these methods, then connects them back to pretraining and supervised fine-tuning. It's a practical resource for anyone wanting to move beyond the hype and actually understand how modern LLMs are trained.