Why RL Post-Training Makes Models “Dumber” in Places

Technical Context

I often see the same reaction: a new post-training release comes out, the model performs better on demos and benchmarks, so it must be smarter overall. Unfortunately, that's not how it works. RL post-training almost always pushes the model towards where a specific reward increases, not towards maintaining broad universality.

In practical terms, this is the typical cost of AI implementation driven by clear KPIs. I optimize the system for instruction-following, preference win-rate, math accuracy, or safe response style, and the model starts to live more tightly within that corridor. In popular scenarios, this yields improvement. In rare, strange, unaccounted tasks, subtle regressions start to appear.

I've dug into such pipelines multiple times, and the most common side effects are familiar: reward hacking, entropy collapse, overfitting to proxy metrics. The model learns to do not what I intended, but what pays off better according to the reward function. So it may look tidier, more confident, and more obedient, while slightly worse at handling unexpected turns in a query.

This is especially amusing with reasoning models. I can boost step-by-step correctness on math or code, but simultaneously degrade calibration, solution diversity, or behavior outside a narrow answer format. Not a catastrophe, more like death by a thousand cuts, but in production, these little things eventually surface.

Business and Automation Impact

For AI automation, the takeaway is simple: don't confuse benchmark score gains with system reliability improvements. If your agent handles support, sales, or internal search, it may become better in 80% of frequent interactions and worse in costly rare cases where errors actually cost money.

The second point is about architecture. I wouldn't apply the same post-training to all roles at once. Some places need a polished RL variant, while others benefit more from a broader base model wrapped with rules, validation, and routing.

These are exactly the trade-offs we at Nahornyi AI Lab typically unpack for clients: where aggressive AI integration is appropriate, and where it's better not to squeeze the model for a shiny metric. If your automation has become too “proper” but fails on real-world cases, let's look at your pipeline and build an AI solution development approach that avoids this trap.

We previously explored Simple Self-Distillation, a method that improves code generation without complex RL and verifiers. This approach becomes especially relevant when we see how RL post-training can degrade performance on less common tasks.