reward function
2 stories filed under reward function on Beyond Market Intelligence. The newest of them: “Watch an AI learn Clash Royale defense in a live browser demo” and “Explore How Verifiable Rewards Empower Small Language Models”. Watching a policy learn is different from reading about it. Training a small language model to reason well comes down to more than architecture. 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 reward function story on Beyond Market Intelligence, newest first.
Watch an AI learn Clash Royale defense in a live browser demo
Watching a policy learn is different from reading about it. This live browser demo makes that visible: a 5,629-parameter agent trains with REINFORCE in plain JavaScript, while every rollout runs through the C++ engine compiled to WebAssembly. The chart shows the brute-forced optimum, so the gap between learned behavior and best answer is always in view. One honest detail stands out: Giant vs Cannon traps most runs in a lane placement worth 75% of optimal.
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.