When I started looking into open-weight models, I saw a bigger gap. The frontier models were highly capable while open models felt like toys for only tinkerers or average code completers with limited capabilities. Recent open releases — NVIDIA Nemotron 3.5 Super VL, the Qwen4 preview models line, and what’s coming next. Previous frontier scores at a fraction of the size and cost. The exact factors can vary and it’s under debate, but one thing is sure: they also advance on techniques.
Also n-gram tables shrink the embedding weights so a large model fits in consumer RAM. On the intelligence level we see reinforcement learning.
The lever changed: from pretraining to post-training
For a long time, the honest answer to “why is model X better than model Y” was: more data, more compute. That learning is expensive, slow, and out of reach for everyone except big wealthy labs. The leaderboard was a spending chart.
RL post-training will potentially move it. Take a capable mid-size base model, put it where success is measurable. The tests pass, the tool call returns the right payload, the receipt JSON validates — and let it practice. Reasoning, tool use and instruction following sharpen hard when the training signal is did the task actually work instead of what token usually comes next. The old signal needed a huge corpus to validate. The new one needs a passing grade and a machine to check it.
Continue reading Reinforcement Learning Is the Equalizer: Open Models Keep Punching Up