Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B
A new study finds that simple repeated sampling often outperforms complex self-reflection methods like Reflexion when token costs are equalized.
- Simple repeated sampling often outperforms complex self-reflection methods like Reflexion.
- Previous gains attributed to reflection may actually result from increased token usage.
- The study tested models from 1.5B to 7B parameters under equal token budget constraints.
Recent research challenges the effectiveness of advanced inference methods like Self-Refine and Reflexion. These techniques, which involve models critiquing and rewriting their own outputs, have been popular for improving accuracy. However, this paper argues that their success might simply be due to the increased volume of text generated rather than the specific reasoning strategies employed.
The authors conducted a fair comparison by equalizing the token budget across different methods. They compared the complex self-reflection approaches against a simple baseline of repeatedly sampling the same question and selecting the most common answer. This baseline proved to be highly competitive.
Testing models ranging from 1.5 billion to 7 billion parameters, the study found that the simple repeated sampling strategy frequently won. This suggests that for many tasks, spending compute on generating more diverse samples is more effective than spending it on iterative self-correction.
Shows that simple sampling strategies might be more cost-effective than complex prompting for inference.
Highlights potential cost savings by using simpler inference methods instead of token-heavy reflection.
Reveals that some AI efficiency startups might be solving problems that can be addressed with brute force compute.
- Repeated Sampling
- Generating multiple outputs for the same input and selecting the best or most common one.
- Self-Refine
- A prompting strategy where a model generates an initial draft and then iteratively improves it.
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