RL
RL at Beyond Market Intelligence is a file of 6 stories. The newest of them: “Explore Xiaomi’s MiMo-V2.6: AI Model Training Achieves $3.5M Benchmark”, “Is Reinforcement Learning Really Needed for Jev's Spreadsheet AI?”, and “Understanding Adam's limits can transform how you train complex models.”. Xiaomi's MiMo-V2.6 just made a compelling case for efficiency in AI training, clocking in at a $3.5 million total RL cost. The question cuts to the heart of Jev's design. 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 RL story on Beyond Market Intelligence, newest first.
Explore Xiaomi’s MiMo-V2.6: AI Model Training Achieves $3.5M Benchmark
Xiaomi's MiMo-V2.6 just made a compelling case for efficiency in AI training, clocking in at a $3.5 million total RL cost. That's a number that invites a second look, especially when paired with a live benchmaxxing dashboard that puts the process on display. It's a practical, transparent approach to a field often shrouded in complexity. For those wrestling with how to evaluate such models, our piece on verifying your AI's understanding offers a timely starting point. This isn't about hype; it's about measurable progress.
Is Reinforcement Learning Really Needed for Jev's Spreadsheet AI?
The question cuts to the heart of Jev's design. If the model only predicts Choice, Score, or Noul, those outputs are already differentiable through cross-entropy or MSE. Adding reinforcement learning feels like extra machinery unless there's a hidden reward signal we're not seeing. The environment would need to be defined, and that's unclear. It's fair to ask if this is substance or just a buzzword. We're not dismissing the approach, but the burden of proof is on the implementation.
Understanding Adam's limits can transform how you train complex models.
Blindly defaulting to Adam without understanding what it's actually doing will cost you. That's the takeaway from a Reddit user whose work in reinforcement learning forced a rethink: Adam still works, but only if you coax it, and its loss "burstiness" can feel maddening. The mathematical intuitions matter. This piece re-covers where Adam fails spectacularly, and for anyone training deep transformers, it's a necessary read. For broader context on model behavior, our related piece on paragraph structure in LLMs is worth exploring.
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.
Exploring world models in robot learning through JEPA's practical promise
Questioning a promising paradigm is how good research gets done. JEPA-style models are compelling, but the recent evangelism from Yann LeCun warrants scrutiny. The biggest downside isn't the architecture itself; it's the overconfidence in its superiority over LLMs and RL. Those systems, for all their flaws, have proven scalable. JEPA's reliance on energy-based learning and latent variables introduces significant training instability challenges that are often glossed over.