Agent loops can quietly turn your eval set into training data
Vegetable-Rub-8241 · reddit · 2026-07-29
Your eval set stops being a true eval set once an agent can optimize against it.
- The core failure mode: selection itself becomes a weak form of training. If you run many candidates through the same fixed benchmark and keep the winner, you are partly fitting benchmark noise.
- Faster agent loops make the problem worse because they burn through the same held-out set more quickly.
- What helped in practice:
- Count every trial, including discarded candidates, so deflated scoring is honest.
- Reveal eval data in blocks instead of keeping one fixed held-out set.
- Put the scorer behind a tool boundary the agent cannot write to; in their case, deterministic code behind MCP servers.
- They also dropped a robustness metric because the search learned to game it, and they compare the LLM loop against a non-LLM genetic programming baseline using the same controller and scoring seam.
The post asks a practical question: in any agentic self-improvement loop, what actually prevents the benchmark from quietly turning into a training set?
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