How Simple Sampling Makes Your Base Model Smarter Without More Training
Researchers found a way to get more thinking out of language models just by asking them in a smarter way, and it works with the model you already have.
Instead of changing the model, the trick is to use repeated sampling from its own answers, checking which lines seem stronger, then try again — like taking small votes inside the model.
The result is better reasoning on hard tasks, from math problems to short coding questions, and it sometimes even beats models that were taught extra skills.
It also keeps answer diversity, so you don't get the same bland reply every time, and you don't need extra data or a verifier.
The cool part: no extra training, no big libraries, just the model itself doing more of its thinking out loud.
You can try this idea on many tasks, and you might be surprised how much smarter the base model seems, it looks simple but it works, and it save time and effort.
Read article comprehensive review in Paperium.net:
Reasoning with Sampling: Your Base Model is Smarter Than You Think
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