RL with Verifiable Rewards (RLVR): from confusing metrics to robust, game-proof policies

I wrote a practical guide to RLVR focused on shipping models that don’t game the reward.
Covers: reading Reward/KL/Entropy as one system, layered verifiable rewards (structure → semantics → behavior), curriculum scheduling, safety/latency/cost gates, and a starter TRL config + reward snippets you can drop in.

Link: https://pavankunchalapk.medium.com/the-complete-guide-to-mastering-rlvr-from-confusing-metrics-to-bulletproof-rewards-7cb1ee736b08

Would love critique—especially real-world failure modes, metric traps, or better gating strategies.

P.S. I'm currently looking for my next role in the LLM / Computer Vision space and would love to connect about any opportunities

Portfolio: Pavan Kunchala - AI Engineer & Full-Stack Developer.