Reasoning with Sampling: Your Base Model is Smarter Than You Think
Researchers found a way to get more thinking out of language models by using repeated sampling from the model's own answers, checking which lines seem stronger, and trying again - like taking small votes inside the model.
Why it matters
This technique could help unlock more reasoning power from existing language models, potentially improving their performance on a wide range of tasks without the need for costly retraining.
Key Points
- 1Repeated sampling from the model's own answers can improve reasoning on hard tasks
- 2The technique works with the base model you already have, without additional training
- 3It maintains answer diversity and doesn't require extra data or a verifier
- 4The base model can become smarter just by doing more of its thinking out loud
Details
The article discusses a technique where researchers found a way to get more reasoning capability out of language models without changing the model itself. The trick is to use repeated sampling from the model's own answers, checking which lines seem stronger, and then trying again - like taking small votes inside the model. This results in better reasoning on tasks like math problems and coding questions, sometimes even outperforming models that were trained on additional skills. The key benefits are that it maintains answer diversity, doesn't require extra data or a verifier, and can make the base model seem smarter without any additional training. The technique is simple to implement and can be applied to many different tasks, potentially saving time and effort compared to training a new, more capable model.
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