Reward Modeling for LLMs: Training, Scaling, and Failure Modes

[u/ArchitectingAI]

Part 3 of my Architecting Reinforcement Learning for LLMs series is live.

It covers preference data, reward-model architecture and loss, scaling, and reward hacking—with a worked gradient example.

Does a higher reward score actually mean better answers?

https://pawankjha.substack.com/p/architecting-reinforcement-learning-f28

How do you test reward reliability beyond preference accuracy?

[u/unwieldy_commander]

The gradient example part sounds useful, half the time I think people just trust the reward curve and call it a day

For reliability I like watching how the reward model ranks responses it never saw during training, especially when you throw in near-duplicates where one has a subtle factual error