As AI models continue to evolve, it’s becoming increasingly important to develop robust, game-proof policies that can’t be manipulated or gamed. That’s where RL with Verifiable Rewards (RLVR) comes in.
RLVR is a practical approach to shipping models that don’t game the reward. But, getting it right can be tricky. That’s why I was excited to come across a comprehensive guide to mastering RLVR, written by Pavan Kunchala.
The guide covers some fascinating topics, including:
* Reading Reward/KL/Entropy as one system
* Layered verifiable rewards (structure → semantics → behavior)
* Curriculum scheduling
* Safety/latency/cost gates
* A starter TRL config + reward snippets you can drop in
One of the most valuable aspects of the guide is its focus on real-world applications and potential pitfalls. Pavan shares his experience and insights on how to avoid common metric traps and failure modes.
Whether you’re a seasoned AI engineer or just starting out, this guide is definitely worth a read. And, if you have any experience with RLVR, Pavan would love to hear your thoughts and feedback.
You can check out the full guide on Medium: https://pavankunchalapk.medium.com/the-complete-guide-to-mastering-rlvr-from-confusing-metrics-to-bulletproof-rewards-7cb1ee736b08
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P.S. Pavan is currently looking for his next role in the LLM / Computer Vision space. If you know of any opportunities, feel free to connect with him.