My Agent Found Real Improvements. The Statistics Still Killed the Promotion.
Previously: 9 Bugs That All Looked Like a Working System · I Built an AI That Rewrites Its Own Prompts · The Edit That Fixed 4 Tasks and Broke 1 · The Gate Is the Product · The Doctor Who Diagnosed Every Patient · 4 Models, 0 Promotable Edits In v0.1.0, an edit fixed 4 tasks and broke 1. Net +3 on 26 tasks. p=0.23. Gate rejected. The ceiling was clear: if you do not move enough tasks, the gate…
The story discusses the challenges of self-editing AI models, specifically Mistral 24B, and the limitations encountered during a promotion process. The source material highlights several key points:
1. The initial edit fixed four tasks out of 26, resulting in a +3 net improvement and a p-value of 0.23, indicating statistical significance. However, the gate rejected the edit due to its effect size being too small.
2. In version 0.2.0, the corpus was expanded to 40 tasks, and the analyzer was improved with rejection context and stronger models. Despite these enhancements, the edit only fixed 1-2 tasks, leading to a mean delta of only +0.025 on 40 tasks. The p-value increased to approximately 0.5, suggesting the results were likely due to random chance.
3. The source emphasizes that simply adding more tasks does not necessarily improve the chances of finding a promotable edit. The improvement in the statistical power argument relies on holding the effect size constant, which was not the case in this scenario. The analyzer's ability to find edits that move enough tasks remained limited.
4. The v0.2.0 edit introduced a side effect of adding "urgent" to tasks where it was not initially present, causing it to fail the ExactMatch scorer. The v0.2.0 version addressed this issue with label-set-aware scorers, but the fundamental problem still exists. The analyzer struggles to find edits that move enough tasks without introducing over-corrections.
5. The source concludes that the gate's rejection of the edit is accurate given the statistical evidence. The task set is large enough, and the pipeline appears correct. The main bottleneck is the analyzer's inability to find edits strong enough to clear the gate. The statistical ceiling is real, and more tasks do not necessarily lead to better results if the search for effective edits is insufficient.
In summary, the story discusses the difficulties faced in self-editing AI models and the limitations encountered when trying to find sufficient improvements to clear the gate for promotion. It highlights the importance of having a robust search algorithm to find edits that move enough tasks, rather than relying solely on increasing the corpus size.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
