Jev has sparked a compelling question: why generate a paragraph when your agent just needs to make a decision?

Choosing a route, classifying an input, selecting the next action—many steps in an AI workflow need a clear choice from known options.

That’s the idea behind ReJev, an independent community project exploring Jev-style decision-making with MiniCPM5-2B. Through LoRA post-training, it adapts our open 2B model to a focused task:
State + question + candidate options → one decision.
On the project’s sealed holdout of 1,892 samples:
📈 Accuracy rose from 51.11% to 80.50% (+29.39 percentage points)
🎯 0% invalid outputs in the reported evaluation
💰 ~$5.31 in cumulative Modal app billing, including earlier experimental overhead

What makes this interesting goes beyond the accuracy gain: it gives developers a concrete experiment in teaching a small, open model to make bounded decisions—the kind of capability worth exploring for agent routing and workflow control.

This is an early, task-specific result, rather than evidence of parity with Jev. But it opens up a practical question for builders:

Which decisions in your agent stack could a specialized 2B model handle?
🔗 Explore ReJev：http://github.com/Joe-rq/ReJev
🤗 Build with MiniCPM5-2B：http://huggingface.co/openbmb/MiniCPM5-2B