8 ms·
I had a similar experience in search and found even holdouts can be overfit to. IE through brute force, it may not see the holdout, but if you gate a change on
by softwaredoug 2mo ago
I had a similar experience in search and found even holdouts can be overfit to. IE through brute force, it may not see the holdout, but if you gate a change on holdout acceptance it will land on a solution that’s overfit to it by somewhat random chance.
The other problem is that holdouts / data inaccessible to the agent isn’t easy to do in most coding agents. It’s not as simple as splitting training data 80% and giving some to the agent and hiding 20%. The agent can figure out where its data came from and find ways to reconstruct / cheat the holdout data.
All the ways of doing this seem annoying: ie having a second project that accepts / rejects changes.
I opted to just build my own harness for these things to avoid overfitting.
https://softwaredoug.com/blog/2026/05/17/autoresearching-a-better-msmarco-bm25 https://softwaredoug.com/blog/2026/05/17/autoresearching-a-b...
- internet_points 2mo agoIf you repeatedly use the same holdout and trigger acceptance on the holdout, it is no longer a holdout but just another training set.
- softwaredoug 2mo agoExactly
- dilyevsky 2mo agoarticle description is low on details but i don't think what you and OP are describing can be categorized as overfitting in the statistical sense and more like "reward hacking" by the model. like you said more likely than not the agent had access to bench source code and just fitted solution to that.