GRPO
GRPO at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Explore How Verifiable Rewards Empower Small Language Models”, “Demystifying the math behind the algorithms powering modern LLMs”, and “Recreating AI alignment results on consumer hardware reveals subtle training challenges.”. Training a small language model to reason well comes down to more than architecture. 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. 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 GRPO story on Beyond Market Intelligence, newest first.
Explore How Verifiable Rewards Empower Small Language Models
Training a small language model to reason well comes down to more than architecture. In a new walkthrough, we explore how GRPO turns verifiable rewards into a lever for local experiments with Unsloth, and why the reward function often shapes outcomes as much as the model itself. It is a practical look at mechanics that are easy to overlook. For a deeper take on how structure guides model behavior, our related piece on paragraph structure in token space pairs naturally with this one.
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.
Recreating AI alignment results on consumer hardware reveals subtle training challenges.
A GRPO run that moves a trait only +2.4 points when you need +15 isn't a slow start; it's a signal that something structural is off. Feedback aligns with what many small-scale RL practitioners suspect: prompt diversity and per-example rubrics matter more than raw compute. If you're exploring how reward design shapes outcomes, our piece on verifiable rewards for small models covers adjacent mechanics. For now, treat this as a prompt-engineering problem, not a training problem.