Foundations
Evaluating RL-Tuned Models
Standard NLP benchmarks break silently when applied to RL-tuned models because the training objective optimises for a reward signal that can diverge from genuine capability, requiring a distinct evaluation stack to distinguish real improvement from reward gaming.
advanced · 9 min read · Premium
When OpenAI published results for their RLHF-trained summarisation model in 2020, they noted something uncomfortable: a model that scored higher on the learned reward signal produced summaries that humans actually preferred, yet that same reward signal could be over-optimised to produce text that scored high while being hollow. The proxy had become the target, and the target had quietly moved. That tension sits at the heart of evaluating any RL-tuned model.
Why Standard Benchmarks Mislead
Pre-training and supervised fine-tuning (SFT) benchmarks such as MMLU, HellaSwag, or HumanEval are designed to probe raw knowledge and surface-level instruction following. They assume a relatively well-behaved distribution shift between training and evaluation. RL post-training breaks that assumption in at least three ways.
Keep reading with Pro.
You're reading the preview. Unlock the full concept plus the library, study plans, the AI mentor, and daily emails.